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- Data Mining
- Crucial Concepts in Data Mining
- Data Warehousing
- On-Line Analytic Processing (OLAP)
- Exploratory Data Analysis (EDA) and Data Mining Techniques
- Neural Networks
Data Mining is an analytic process designed to explore data (usually large amounts of data – typically business or market related – also known as “big data”) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction – and predictive data mining is the most common type of data mining and one that has the most direct business applications. The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identification with validation/verification, and (3) deployment (i.e., the application of the model to new data in order to generate predictions).
Stage 1: Exploration. This stage usually starts with data preparation which may involve cleaning data, data transformations, selecting subsets of records and – in case of data sets with large numbers of variables (“fields”) – performing some preliminary feature selection operations to bring the number of variables to a manageable range (depending on the statistical methods which are being considered). Then, depending on the nature of the analytic problem, this first stage of the process of data mining may involve anywhere between a simple choice of straightforward predictors for a regression model, to elaborate exploratory analyses using a wide variety of graphical and statistical methods (see Exploratory Data Analysis (EDA)) in order to identify the most relevant variables and determine the complexity and/or the general nature of models that can be taken into account in the next stage.
Stage 2: Model building and validation. This stage involves considering various models and choosing the best one based on their predictive performance (i.e., explaining the variability in question and producing stable results across samples). This may sound like a simple operation, but in fact, it sometimes involves a very elaborate process. There are a variety of techniques developed to achieve that goal – many of which are based on so-called “competitive evaluation of models,” that is, applying different models to the same data set and then comparing their performance to choose the best. These techniques – which are often considered the core of predictive data mining – include: Bagging (Voting, Averaging), Boosting, Stacking (Stacked Generalizations), and Meta-Learning.
Stage 3: Deployment. That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome.
The concept of Data Mining is becoming increasingly popular as a business information management tool where it is expected to reveal knowledge structures that can guide decisions in conditions of limited certainty. Recently, there has been increased interest in developing new analytic techniques specifically designed to address the issues relevant to business Data Mining (e.g., Classification Trees), but Data Mining is still based on the conceptual principles of statistics including the traditional Exploratory Data Analysis (EDA) and modeling and it shares with them both some components of its general approaches and specific techniques.
However, an important general difference in the focus and purpose between Data Mining and the traditional Exploratory Data Analysis (EDA) is that Data Mining is more oriented towards applications than the basic nature of the underlying phenomena. In other words, Data Mining is relatively less concerned with identifying the specific relations between the involved variables. For example, uncovering the nature of the underlying functions or the specific types of interactive, multivariate dependencies between variables are not the main goal of Data Mining. Instead, the focus is on producing a solution that can generate useful predictions. Therefore, Data Mining accepts among others a “black box” approach to data exploration or knowledge discovery and uses not only the traditional Exploratory Data Analysis (EDA) techniques, but also such techniques as Neural Networks which can generate valid predictions but are not capable of identifying the specific nature of the interrelations between the variables on which the predictions are based.
Data Mining is often considered to be “a blend of statistics, AI (artificial intelligence), and data base research” (Pregibon, 1997, p. 8), which until very recently was not commonly recognized as a field of interest for statisticians, and was even considered by some “a dirty word in Statistics” (Pregibon, 1997, p. 8). Due to its applied importance, however, the field emerges as a rapidly growing and major area (also in statistics) where important theoretical advances are being made (see, for example, the recent annual International Conferences on Knowledge Discovery and Data Mining, co-hosted by the American Statistical Association).
For information on Data Mining techniques, review the summary topics included below. There are numerous books that review the theory and practice of data mining; the following books offer a representative sample of recent general books on data mining, representing a variety of approaches and perspectives:
Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley.
Edelstein, H., A. (1999). Introduction to data mining and knowledge discovery (3rd ed). Potomac, MD: Two Crows Corp.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery & data mining. Cambridge, MA: MIT Press.
Han, J., Kamber, M. (2000). Data mining: Concepts and Techniques. New York: Morgan-Kaufman.
Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning : Data mining, inference, and prediction. New York: Springer.
Pregibon, D. (1997). Data Mining. Statistical Computing and Graphics, 7, 8.
Weiss, S. M., & Indurkhya, N. (1997). Predictive data mining: A practical guide. New York: Morgan-Kaufman.
Westphal, C., Blaxton, T. (1998). Data mining solutions. New York: Wiley.
Witten, I. H., & Frank, E. (2000). Data mining. New York: Morgan-Kaufmann.
Bagging (Voting, Averaging)
The concept of bagging (voting for classification, averaging for regression-type problems with continuous dependent variables of interest) applies to the area of predictive data mining, to combine the predicted classifications (prediction) from multiple models, or from the same type of model for different learning data. It is also used to address the inherent instability of results when applying complex models to relatively small data sets. Suppose your data mining task is to build a model for predictive classification, and the dataset from which to train the model (learning data set, which contains observed classifications) is relatively small. You could repeatedly sub-sample (with replacement) from the dataset, and apply, for example, a tree classifier (e.g., C&RT and CHAID) to the successive samples. In practice, very different trees will often be grown for the different samples, illustrating the instability of models often evident with small data sets. One method of deriving a single prediction (for new observations) is to use all trees found in the different samples, and to apply some simple voting: The final classification is the one most often predicted by the different trees. Note that some weighted combination of predictions (weighted vote, weighted average) is also possible, and commonly used. A sophisticated (machine learning) algorithm for generating weights for weighted prediction or voting is the Boosting procedure.
The concept of boosting applies to the area of predictive data mining, to generate multiple models or classifiers (for prediction or classification), and to derive weights to combine the predictions from those models into a single prediction or predicted classification (see also Bagging).
A simple algorithm for boosting works like this: Start by applying some method (e.g., a tree classifier such as C&RT or CHAID) to the learning data, where each observation is assigned an equal weight. Compute the predicted classifications, and apply weights to the observations in the learning sample that are inversely proportional to the accuracy of the classification. In other words, assign greater weight to those observations that were difficult to classify (where the misclassification rate was high), and lower weights to those that were easy to classify (where the misclassification rate was low). In the context of C&RT for example, different misclassification costs (for the different classes) can be applied, inversely proportional to the accuracy of prediction in each class. Then apply the classifier again to the weighted data (or with different misclassification costs), and continue with the next iteration (application of the analysis method for classification to the re-weighted data).
Boosting will generate a sequence of classifiers, where each consecutive classifier in the sequence is an “expert” in classifying observations that were not well classified by those preceding it. During deployment (for prediction or classification of new cases), the predictions from the different classifiers can then be combined (e.g., via voting, or some weighted voting procedure) to derive a single best prediction or classification.
Note that boosting can also be applied to learning methods that do not explicitly support weights or misclassification costs. In that case, random sub-sampling can be applied to the learning data in the successive steps of the iterative boosting procedure, where the probability for selection of an observation into the subsample is inversely proportional to the accuracy of the prediction for that observation in the previous iteration (in the sequence of iterations of the boosting procedure).
See Models for Data Mining.
Data Preparation (in Data Mining)
Data preparation and cleaning is an often neglected but extremely important step in the data mining process. The old saying “garbage-in-garbage-out” is particularly applicable to the typical data mining projects where large data sets collected via some automatic methods (e.g., via the Web) serve as the input into the analyses. Often, the method by which the data where gathered was not tightly controlled, and so the data may contain out-of-range values (e.g., Income: -100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), and the like. Analyzing data that has not been carefully screened for such problems can produce highly misleading results, in particular in predictive data mining.
Data Reduction (for Data Mining)
The term Data Reduction in the context of data mining is usually applied to projects where the goal is to aggregate or amalgamate the information contained in large datasets into manageable (smaller) information nuggets. Data reduction methods can include simple tabulation, aggregation (computing descriptive statistics) or more sophisticated techniques like clustering, principal components analysis, etc.
The concept of deployment in predictive data mining refers to the application of a model for prediction or classification to new data. After a satisfactory model or set of models has been identified (trained) for a particular application, we usually want to deploy those models so that predictions or predicted classifications can quickly be obtained for new data. For example, a credit card company may want to deploy a trained model or set of models (e.g., neural networks, meta-learner) to quickly identify transactions which have a high probability of being fraudulent.
The concept of drill-down analysis applies to the area of data mining, to denote the interactive exploration of data, in particular of large databases. The process of drill-down analyses begins by considering some simple break-downs of the data by a few variables of interest (e.g., Gender, geographic region, etc.). Various statistics, tables, histograms, and other graphical summaries can be computed for each group. Next, we may want to “drill-down” to expose and further analyze the data “underneath” one of the categorizations, for example, we might want to further review the data for males from the mid-west. Again, various statistical and graphical summaries can be computed for those cases only, which might suggest further break-downs by other variables (e.g., income, age, etc.). At the lowest (“bottom”) level are the raw data: For example, you may want to review the addresses of male customers from one region, for a certain income group, etc., and to offer to those customers some particular services of particular utility to that group.
One of the preliminary stage in predictive data mining, when the data set includes more variables than could be included (or would be efficient to include) in the actual model building phase (or even in initial exploratory operations), is to select predictors from a large list of candidates. For example, when data are collected via automated (computerized) methods, it is not uncommon that measurements are recorded for thousands or hundreds of thousands (or more) of predictors. The standard analytic methods for predictive data mining, such as neural network analyses, classification and regression trees, generalized linear models, or general linear models become impractical when the number of predictors exceed more than a few hundred variables.
Feature selection selects a subset of predictors from a large list of candidate predictors without assuming that the relationships between the predictors and the dependent or outcome variables of interest are linear, or even monotone. Therefore, this is used as a pre-processor for predictive data mining, to select manageable sets of predictors that are likely related to the dependent (outcome) variables of interest, for further analyses with any of the other methods for regression and classification.
Machine learning, computational learning theory, and similar terms are often used in the context of Data Mining, to denote the application of generic model-fitting or classification algorithms for predictive data mining. Unlike traditional statistical data analysis, which is usually concerned with the estimation of population parameters by statistical inference, the emphasis in data mining (and machine learning) is usually on the accuracy of prediction (predicted classification), regardless of whether or not the “models” or techniques that are used to generate the prediction is interpretable or open to simple explanation. Good examples of this type of technique often applied to predictive data mining are neural networks or meta-learning techniques such as boosting, etc. These methods usually involve the fitting of very complex “generic” models, that are not related to any reasoning or theoretical understanding of underlying causal processes; instead, these techniques can be shown to generate accurate predictions or classification in crossvalidation samples.
The concept of meta-learning applies to the area of predictive data mining, to combine the predictions from multiple models. It is particularly useful when the types of models included in the project are very different. In this context, this procedure is also referred to as Stacking (Stacked Generalization).
Suppose your data mining project includes tree classifiers, such as C&RT and CHAID, linear discriminant analysis (e.g., see GDA), and Neural Networks. Each computes predicted classifications for a crossvalidation sample, from which overall goodness-of-fit statistics (e.g., misclassification rates) can be computed. Experience has shown that combining the predictions from multiple methods often yields more accurate predictions than can be derived from any one method (e.g., see Witten and Frank, 2000). The predictions from different classifiers can be used as input into a meta-learner, which will attempt to combine the predictions to create a final best predicted classification. So, for example, the predicted classifications from the tree classifiers, linear model, and the neural network classifier(s) can be used as input variables into a neural network meta-classifier, which will attempt to “learn” from the data how to combine the predictions from the different models to yield maximum classification accuracy.
We can apply meta-learners to the results from different meta-learners to create “meta-meta”-learners, and so on; however, in practice such exponential increase in the amount of data processing, in order to derive an accurate prediction, will yield less and less marginal utility.
Models for Data Mining
In the business environment, complex data mining projects may require the coordinate efforts of various experts, stakeholders, or departments throughout an entire organization. In the data mining literature, various “general frameworks” have been proposed to serve as blueprints for how to organize the process of gathering data, analyzing data, disseminating results, implementing results, and monitoring improvements.
One such model, CRISP (Cross-Industry Standard Process for data mining) was proposed in the mid-1990s by a European consortium of companies to serve as a non-proprietary standard process model for data mining. This general approach postulates the following (perhaps not particularly controversial) general sequence of steps for data mining projects:
Another approach – the Six Sigma methodology – is a well-structured, data-driven methodology for eliminating defects, waste, or quality control problems of all kinds in manufacturing, service delivery, management, and other business activities. This model has recently become very popular (due to its successful implementations) in various American industries, and it appears to gain favor worldwide. It postulated a sequence of, so-called, DMAIC steps –
– that grew up from the manufacturing, quality improvement, and process control traditions and is particularly well suited to production environments (including “production of services,” i.e., service industries).
Another framework of this kind (actually somewhat similar to Six Sigma) is the approach proposed by SAS Institute called SEMMA –
– which is focusing more on the technical activities typically involved in a data mining project.
All of these models are concerned with the process of how to integrate data mining methodology into an organization, how to “convert data into information,” how to involve important stake-holders, and how to disseminate the information in a form that can easily be converted by stake-holders into resources for strategic decision making.
Some software tools for data mining are specifically designed and documented to fit into one of these specific frameworks.
The general underlying philosophy of StatSoft’s STATISTICA Data Miner is to provide a flexible data mining workbench that can be integrated into any organization, industry, or organizational culture, regardless of the general data mining process-model that the organization chooses to adopt. For example, STATISTICA Data Miner can include the complete set of (specific) necessary tools for ongoing company wide Six Sigma quality control efforts, and users can take advantage of its (still optional) DMAIC-centric user interface for industrial data mining tools. It can equally well be integrated into ongoing marketing research, CRM (Customer Relationship Management) projects, etc. that follow either the CRISP or SEMMA approach – it fits both of them perfectly well without favoring either one. Also, STATISTICA Data Miner offers all the advantages of a general data mining oriented “development kit” that includes easy to use tools for incorporating into your projects not only such components as custom database gateway solutions, prompted interactive queries, or proprietary algorithms, but also systems of access privileges, workgroup management, and other collaborative work tools that allow you to design large scale, enterprise-wide systems (e.g., following the CRISP, SEMMA, or a combination of both models) that involve your entire organization.
Predictive Data Mining
The term Predictive Data Mining is usually applied to identify data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models (e.g., neural networks, meta-learner) that can quickly identify transactions which have a high probability of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g., to identify cluster or segments of customers), in which case drill-down descriptive and exploratory methods would be applied. Data reduction is another possible objective for data mining (e.g., to aggregate or amalgamate the information in very large data sets into useful and manageable chunks).
See Models for Data Mining.
Stacking (Stacked Generalization)
The concept of stacking (Stacked Generalization) applies to the area of predictive data mining, to combine the predictions from multiple models. It is particularly useful when the types of models included in the project are very different.
Suppose your data mining project includes tree classifiers, such as C&RT or CHAID, linear discriminant analysis (e.g., see GDA), and Neural Networks. Each computes predicted classifications for a crossvalidation sample, from which overall goodness-of-fit statistics (e.g., misclassification rates) can be computed. Experience has shown that combining the predictions from multiple methods often yields more accurate predictions than can be derived from any one method (e.g., see Witten and Frank, 2000). In stacking, the predictions from different classifiers are used as input into a meta-learner, which attempts to combine the predictions to create a final best predicted classification. So, for example, the predicted classifications from the tree classifiers, linear model, and the neural network classifier(s) can be used as input variables into a neural network meta-classifier, which will attempt to “learn” from the data how to combine the predictions from the different models to yield maximum classification accuracy.
While Data Mining is typically concerned with the detection of patterns in numeric data, very often important (e.g., critical to business) information is stored in the form of text. Unlike numeric data, text is often amorphous, and difficult to deal with. Text mining generally consists of the analysis of (multiple) text documents by extracting key phrases, concepts, etc. and the preparation of the text processed in that manner for further analyses with numeric data mining techniques (e.g., to determine co-occurrences of concepts, key phrases, names, addresses, product names, etc.).
StatSoft defines data warehousing as a process of organizing the storage of large, multivariate data sets in a way that facilitates the retrieval of information for analytic purposes.
The most efficient data warehousing architecture will be capable of incorporating or at least referencing all data available in the relevant enterprise-wide information management systems, using designated technology suitable for corporate data base management (e.g., Oracle, Sybase, MS SQL Server. Also, a flexible, high-performance (see the IDP technology), open architecture approach to data warehousing – that flexibly integrates with the existing corporate systems and allows the users to organize and efficiently reference for analytic purposes enterprise repositories of data of practically any complexity – is offered in StatSoft enterprise systems such as STATISTICA Enterprise and STATISTICA Enterprise/QC , which can also work in conjunction with STATISTICA Data Miner and STATISTICA Enterprise Server.
The term On-Line Analytic Processing – OLAP (or Fast Analysis of Shared Multidimensional Information – FASMI) refers to technology that allows users of multidimensional databases to generate on-line descriptive or comparative summaries (“views”) of data and other analytic queries. Note that despite its name, analyses referred to as OLAP do not need to be performed truly “on-line” (or in real-time); the term applies to analyses of multidimensional databases (that may, obviously, contain dynamically updated information) through efficient “multidimensional” queries that reference various types of data. OLAP facilities can be integrated into corporate (enterprise-wide) database systems and they allow analysts and managers to monitor the performance of the business (e.g., such as various aspects of the manufacturing process or numbers and types of completed transactions at different locations) or the market. The final result of OLAP techniques can be very simple (e.g., frequency tables, descriptive statistics, simple cross-tabulations) or more complex (e.g., they may involve seasonal adjustments, removal of outliers, and other forms of cleaning the data). Although Data Mining techniques can operate on any kind of unprocessed or even unstructured information, they can also be applied to the data views and summaries generated by OLAP to provide more in-depth and often more multidimensional knowledge. In this sense, Data Mining techniques could be considered to represent either a different analytic approach (serving different purposes than OLAP) or as an analytic extension of OLAP.
As opposed to traditional hypothesis testing designed to verify a priori hypotheses about relations between variables (e.g., “There is a positive correlation between the AGE of a person and his/her RISK TAKING disposition”), exploratory data analysis (EDA) is used to identify systematic relations between variables when there are no (or not complete) a priori expectations as to the nature of those relations. In a typical exploratory data analysis process, many variables are taken into account and compared, using a variety of techniques in the search for systematic patterns.
Computational exploratory data analysis methods include both simple basic statistics and more advanced, designated multivariate exploratory techniques designed to identify patterns in multivariate data sets.
Basic statistical exploratory methods. The basic statistical exploratory methods include such techniques as examining distributions of variables (e.g., to identify highly skewed or non-normal, such as bi-modal patterns), reviewing large correlation matrices for coefficients that meet certain thresholds (see example above), or examining multi-way frequency tables (e.g., “slice by slice” systematically reviewing combinations of levels of control variables).
Multivariate exploratory techniques. Multivariate exploratory techniques designed specifically to identify patterns in multivariate (or univariate, such as sequences of measurements) data sets include: Cluster Analysis, Factor Analysis, Discriminant Function Analysis, Multidimensional Scaling, Log-linear Analysis, Canonical Correlation, Stepwise Linear and Nonlinear (e.g., Logit) Regression, Correspondence Analysis, Time Series Analysis, and Classification Trees.
Neural Networks. Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data.
A large selection of powerful exploratory data analytic techniques is also offered by graphical data visualization methods that can identify relations, trends, and biases “hidden” in unstructured data sets.
Brushing. Perhaps the most common and historically first widely used technique explicitly identified as graphical exploratory data analysis is brushing, an interactive method allowing us to select on-screen specific data points or subsets of data and identify their (e.g., common) characteristics, or to examine their effects on relations between relevant variables. Those relations between variables can be visualized by fitted functions (e.g., 2D lines or 3D surfaces) and their confidence intervals, thus, for example, we can examine changes in those functions by interactively (temporarily) removing or adding specific subsets of data. For example, one of many applications of the brushing technique is to select (i.e., highlight) in a matrix scatterplot all data points that belong to a certain category (e.g., a “medium” income level, see the highlighted subset in the fourth component graph of the first row in the illustration left) in order to examine how those specific observations contribute to relations between other variables in the same data set (e.g, the correlation between the “debt” and “assets” in the current example). If the brushing facility supports features such as “animated brushing” or “automatic function re-fitting,” we can define a dynamic brush that would move over the consecutive ranges of a criterion variable (e.g., “income” measured on a continuous scale or a discrete [3-level] scale as on the illustration above) and examine the dynamics of the contribution of the criterion variable to the relations between other relevant variables in the same data set.
Other graphical EDA techniques. Other graphical exploratory analytic techniques include function fitting and plotting, data smoothing, overlaying and merging of multiple displays, categorizing data, splitting/merging subsets of data in graphs, aggregating data in graphs, identifying and marking subsets of data that meet specific conditions, icon plots,
shading, plotting confidence intervals and confidence areas (e.g., ellipses),
integrated layered compressions,
with animated stratification (cross-sections) of 3D displays, and selective highlighting of specific series and blocks of data.
The exploration of data can only serve as the first stage of data analysis and its results can be treated as tentative at best as long as they are not confirmed, e.g., crossvalidated, using a different data set (or an independent subset). If the result of the exploratory stage suggests a particular model, then its validity can be verified by applying it to a new data set and testing its fit (e.g., testing its predictive validity). Case selection conditions can be used to quickly define subsets of data (e.g., for estimation and verification), and for testing the robustness of results.
(see also, the Neural Networks topic)
Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data. Neural Networks is one of the Data Mining techniques.
The first step is to design a specific network architecture (that includes a specific number of “layers” each consisting of a certain number of “neurons”). The size and structure of the network needs to match the nature (e.g., the formal complexity) of the investigated phenomenon. Because the latter is obviously not known very well at this early stage, this task is not easy and often involves multiple “trials and errors.” (Now, there is, however, neural network software that applies artificial intelligence techniques to aid in that tedious task and finds “the best” network architecture.)
The new network is then subjected to the process of “training.” In that phase, neurons apply an iterative process to the number of inputs (variables) to adjust the weights of the network in order to optimally predict (in traditional terms, we could say find a “fit” to) the sample data on which the “training” is performed. After the phase of learning from an existing data set, the new network is ready and it can then be used to generate predictions.
The resulting “network” developed in the process of “learning” represents a pattern detected in the data. Thus, in this approach, the “network” is the functional equivalent of a model of relations between variables in the traditional model building approach. However, unlike in the traditional models, in the “network,” those relations cannot be articulated in the usual terms used in statistics or methodology to describe relations between variables (such as, for example, “A is positively correlated with B but only for observations where the value of C is low and D is high”). Some neural networks can produce highly accurate predictions; they represent, however, a typical a-theoretical (one can say, “a black box”) research approach. That approach is concerned only with practical considerations, that is, with the predictive validity of the solution and its applied relevance and not with the nature of the underlying mechanism or its relevance for any “theory” of the underlying phenomena.
However, it should be mentioned that Neural Network techniques can also be used as a component of analyses designed to build explanatory models because Neural Networks can help explore data sets in search for relevant variables or groups of variables; the results of such explorations can then facilitate the process of model building. Moreover, now there is neural network software that uses sophisticated algorithms to search for the most relevant input variables, thus potentially contributing directly to the model building process.
One of the major advantages of neural networks is that, theoretically, they are capable of approximating any continuous function, and thus the researcher does not need to have any hypotheses about the underlying model, or even to some extent, which variables matter. An important disadvantage, however, is that the final solution depends on the initial conditions of the network, and, as stated before, it is virtually impossible to “interpret” the solution in traditional, analytic terms, such as those used to build theories that explain phenomena.
Some authors stress the fact that neural networks use, or we should say are expected to use, massively parallel computation models. For example Haykin (1994) defines neural network as:
“a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: (1) Knowledge is acquired by the network through a learning process, and (2) Interneuron connection strengths known as synaptic weights are used to store the knowledge.” (p. 2).
However, as Ripley (1996) points out, the vast majority of contemporary neural network applications run on single-processor computers and he argues that a large speed-up can be achieved not only by developing software that will take advantage of multiprocessor hardware by also by designing better (more efficient) learning algorithms.
Neural networks is one of the methods used in Data Mining; see also Exploratory Data Analysis. For more information on neural networks, see Haykin (1994), Masters (1995), Ripley (1996), and Welstead (1994). For a discussion of neural networks as statistical tools, see Warner and Misra (1996). See also, STATISTICA Neural Networks.
StatSoft offers statistical consulting services that can range from a few hours of expert advice on your data analysis to a full service analysis or data mining project. The data preparation and analysis tasks take time and expertise. When these resources are not available to complete the analysis project, StatSoft Statistical Consultants can help. Our consultants are very knowledgeable in statistics and experts with the STATISTICA software.
A StatSoft Statistical Consultant will review your project needs and provide an estimate of the time required and cost with no cost or obligation. Email firstname.lastname@example.org to discuss your project.
Statistical Consulting encompasses a wide range of services including:
- Designing an experiment or study before data are collected
- Data cleaning and exploration
- Model building and evaluation
- Deployment of models generated from data mining
Statistical Consultants can either provide advice and guidance at any of these steps or perform the tasks. Consulting projects are custom tailored to your specific needs.
The power industry has been highly successful with implementing STATISTICA in conjunction with consulting services. Visit StatSoft Power Solutions to learn more about how companies in the power industry have reduced costs and improved production with STATISTICA MultiStream.
For the first time, the number of users of free/open source software exceeded the number of users of commercial software. The usage of Big data software grew five-fold. R, Excel, and RapidMiner were the most popular tools, with Statsoft Statistica getting the top commercial tool spot.
The 13th annual KDnuggets Software Poll asked:
About 28% used commercial software but not free software, 30% used free software but not commercial, and 41% used both.
The usage of big data tools grew five-fold: 15% used them in 2012, vs about 3% in 2011.
R, Excel, and RapidMiner are the most popular tools, with Statsoft Statistica becoming the most popular commercial tool, getting more votes from SAS (in part due to more active campaign from Statsoft users, and lack of such campaign from SAS).
Among those who wrote analytics code in lower-level languages, R, SQL, Java, and Python were most popular.
This poll also had a very large number of participants and used email verification and other measures to remove unnatural votes (*see note below).
|What Analytics, Data mining, Big Data software you used in the past 12 months for a real project (not just evaluation) [798 voters]|
|Legend: Free/Open Source tools
| % users in 2012
% users in 2011
|R (245)|| 30.7%
|Excel (238)|| 29.8%
|Rapid-I RapidMiner (213)|| 26.7%
|KNIME (174)|| 21.8%
|Weka / Pentaho (118)|| 14.8%
|StatSoft Statistica (112)|| 14.0%
|SAS (101)|| 12.7%
|Rapid-I RapidAnalytics (83)|| 10.4%
not asked in 2011
|MATLAB (80)|| 10.0%
|IBM SPSS Statistics (62)|| 7.8%
|IBM SPSS Modeler (54)|| 6.8%
|SAS Enterprise Miner (46)|| 5.8%
|Orange (42)|| 5.3%
|Microsoft SQL Server (40)|| 5.0%
|Other free analytics/data mining software (39)|| 4.9%
|TIBCO Spotfire / S+ / Miner (37)|| 4.6%
|Oracle Data Miner (35)|| 4.4%
|Tableau (35)|| 4.4%
|JMP (32)|| 4.0%
|Other commercial analytics/data mining software (32)|| 4.0%
|Mathematica (23)|| 2.9%
|Miner3D (19)|| 2.4%
|IBM Cognos (16)|| 2.0%
not asked in 2011
|Stata (15)|| 1.9%
|Bayesia (14)|| 1.8%
|KXEN (14)|| 1.8%
|Zementis (14)|| 1.8%
|C4.5/C5.0/See5 (13)|| 1.6%
|Revolution Computing (11)|| 1.4%
|Salford SPM/CART/MARS/TreeNet/RF (9)|| 1.1%
|Angoss (7)|| 0.9%
|SAP (including BusinessObjects/Sybase/Hana) (7)|| 0.9%
not asked in 2011
|XLSTAT (7)|| 0.9%
|RapidInsight/Veera (5)|| 0.6%
not asked in 2011
|11 Ants Analytics (4)|| 0.5%
|Teradata Miner (4)|| 0.5%
not asked in 2011
|Predixion Software (3)|| 0.4%
|WordStat (3)|| 0.4%
Among tools with at least 10 users, the tools with the highest increase in “usage percent” were
- Oracle Data Miner, 4.4% in from 2012, up from 0.7% in 2011, 505% increase
- Orange, 5.3% from 1.3%, 315% increase
- TIBCO Spotfire / S+ / Miner, 4.6% from 1.7%, 169% increase
- Stata, 1.9% from 0.8%, 130% increase
- Bayesia, 1.8% from 0.8%, 115% increase
The three tools with highest decrease in usage percent were 11 Ants Analytics, Salford SPM/CART/MARS/TreeNet/RF, and Zementis. Their dramatic decrease is probably due to vendors doing much less (or nothing) to encourage their users to vote in 2012 as compared to 2011.
Note: 3 tools received less than 3 votes and were not included in the above table: Clarabridge, Megaputer Polyanalyst/TextAnalyst, Grapheur/LIONsolver.
Big data tools use grew 5-fold, from about 3% to about 15% of respondents.
|Big Data software you used in the past 12 months|
|Apache Hadoop/Hbase/Pig/Hive (67)||8.4%|
|Amazon Web Services (AWS) (36)||4.5%|
|NoSQL databases (33)||4.1%|
|Other Big Data Data/Cloud analytics software (21)||2.6%|
|Other Hadoop-based tools (10)||1.3%|
We also asked about the popularity of the individual languages for data mining. Note that we also included R in this table, as well as among higher-level tools
|Your own code you used for analytics/data mining in the past 12 months in:|
|Other languages (57)||7.1%|
For comparison here are the recent software polls:
- KDnuggets 2011 Poll: Data Mining/Analytic Tools Used
- KDnuggets 2010 Poll: Data Mining / Analytic Tools Used
- KDnuggets 2009 Poll: Data Mining Tools Used.
Vote: cleaning: To reduce multiple voting this poll used email verification, which reduced the total number of votes compared to 2011, but made results more representative.
Furthermore, some vendors were much more active than others in recruiting their users, and to give a more objective picture of the tool popularity, a large number (over 100) of the “unnatural” votes were removed, leaving 798 votes.
Many entrepreneurs foresee vast profits in mining data from online activity and mobile devices. One Wharton business school professor strongly disagrees.
Few ideas hold more sway among entrepreneurs and investors these days than “Big Data.” The idea is that we are now collecting so much information about people from their online behavior and, especially, through their mobile phones that we can make increasingly specific predictions about how they will behave and what they will buy.
But are those assumptions really true? One doubter is Peter Fader, codirector of the Wharton Customer Analytics Initiative at the University of Pennsylvania, where he is also a professor of marketing. Fader shared some of his concerns in an interview with reporter Lee Gomes.
TR: How would you describe the prevailing idea about Big Data inside the tech community?
Fader: “More is better.” If you can give me more data about a customer—if you can capture more aspects of their behavior, their connections with others, their interests, and so on—then I can pin down exactly what this person is all about. I can anticipate what they will buy, and when, and for how much, and through what channel.
It reminds me a lot of what was going on 15 years ago with CRM (customer relationship management). Back then, the idea was “Wow, we can start collecting all these different transactions and data, and then, boy, think of all the predictions we will be able to make.” But ask anyone today what comes to mind when you say “CRM,” and you’ll hear “frustration,” “disaster,” “expensive,” and “out of control.” It turned out to be a great big IT wild-goose chase. And I’m afraid we’re heading down the same road with Big Data.
There seem to be a lot of businesses these days that promise to take a Twitter stream or a collection of Facebook comments and then make some prediction: about a stock price, about how a product will be received in the market.
That is all ridiculous. If you can get me a really granular view of data—for example, an individual’s tweets and then that same individual’s transactions, so I can see how they are interacting with each other—that’s a whole other story. But that isn’t what is happening. People are focusing on sexy social-media stuff and pushing it much further than they should be.
Some say the data fetish you’re describing is especially epidemic with the many startups connected with mobile computing. Do you think that’s true? And if so, wouldn’t it suggest that a year or two from now, there are going to be a lot of disappointed entrepreneurs and VCs?
There is a “data fetish” with every new trackable technology, from e-mail and Web browsing in the ’90s all the way through mobile communications and geolocation services today. Too many people think that mobile is a “whole new world,” offering stunning insights into behaviors that were inconceivable before. But many of the basic patterns are surprisingly consistent across these platforms. That doesn’t make them uninteresting or unimportant. But the basic methods we can use in the mobile world to understand and forecast these behaviors (and thus the key data needed to accomplish these tasks) are not nearly as radical as many people suspect.
But doesn’t mobile computing provide some forms of data that would be especially helpful, like your location—the fact that at a given moment, you might be shopping in a store? Information like that would seem to be quite valuable.
Absolutely. I’m not a total data Luddite. There’s no question that new technologies will provide all kinds of genuinely useful measures that were previously unattainable. The key question is: Just how much of that data do we really need? For instance, do we need a second-by-second log of the shopper’s location? Would it be truly helpful to integrate this series of observations with other behavioral data (e.g., which products the shopper examined)? Or would this just be nice to know? And how much of this data should we save after the trip is completed?
A true data scientist would have a decent sense of how to answer these questions, with an eye toward practical decision-making. But a Big Data zealot might say, “Save it all—you never know when it might come in handy for a future data-mining expedition.” That’s the distinction that separates “old school” and “new school” analysts.
Surely you’re not against machine learning, which has revolutionized fields like language translation, or new database tools like Hadoop?
I make sure my PhD students learn all these emerging technologies, because they are all very important for certain kinds of tasks. Machine learning is very good at classification—putting things in buckets. If I want to know which brand this person is going to buy next, or if this person is going to vote Republican or Democrat, nothing can touch machine learning, and it’s getting better all the time.
The problem is that there are many decisions that aren’t as easily “bucketized”; for instance, questions about “when” as opposed to “which.” Machine learning can break down pretty dramatically in those tasks. It’s important to have a much broader skill set than just machine learning and database management, but many “big data” people don’t know what they don’t know.
You appear to believe that some of the best work in data science was done long ago.
The golden age for predictive behavior was 40 or 50 years ago, when data were really sparse and companies had to squeeze as much insight as they could from them.
Consider Lester Wunderman, who coined the phrase “direct marketing” in the 1960s. He was doing true data science. He said, “Let’s write down everything we know about this customer: what they bought, what catalogue we sent them, what they paid for it.” It was very hard, because he didn’t have a Hadoop cluster to do it for him.
So what did he discover?
The legacy that he (and other old-school direct marketers) gave us is the still-powerful rubric of RFM: recency, frequency, monetary value.
The “F” and the “M” are obvious. You didn’t need any science for that. The “R” part is the most interesting, because it wasn’t obvious that recency, or the time of the last transaction, should even belong in the triumvirate of key measures, much less be first on the list. But it was discovered that customers who did stuff recently, even if they didn’t do a lot, were more valuable than customers who hadn’t been around for a while. That was a big surprise.
Some of those old models are really phenomenal, even today. Ask anyone in direct marketing about RFM, and they’ll say, “Tell me something I don’t know.” But ask anyone in e-commerce, and they probably won’t know what you’re talking about. Or they will use a lot of Big Data and end up rediscovering the RFM wheel—and that wheel might not run quite as smoothly as the original one.
Big Data and data scientists seem to have such a veneer of respectability.
In investing, you have “technical chartists.” They watch [stock] prices bouncing up and down, hitting what is called “resistance” at 30 or “support” at 20, for example. Chartists are looking at the data without developing fundamental explanations for why those movements are taking place—about the quality of a company’s management, for example.
Among financial academics, chartists tend to be regarded as quacks. But a lot of the Big Data people are exactly like them. They say, “We are just going to stare at the data and look for patterns, and then act on them when we find them.” In short, there is very little real science in what we call “data science,” and that’s a big problem.
Does any industry do it right?
Yes: insurance. Actuaries can say with great confidence what percent of people with your characteristics will live to be 80. But no actuary would ever try to predict when you are going to die. They know exactly where to draw the line.
Even with infinite knowledge of past behavior, we often won’t have enough information to make meaningful predictions about the future. In fact, the more data we have, the more false confidence we will have. Not only won’t our hit rate be perfect, it will be surprisingly low. The important part, as both scientists and businesspeople, is to understand what our limits are and to use the best possible science to fill in the gaps. All the data in the world will never achieve that goal for us.
Capital Equipment Manufacturers utilize STATISTICA throughout the manufacturing process and then analyze the repair and usage data once their products are in use by customers
STATISTICA is an integral part of the quality control and Six Sigma programs at heavy equipment manufacturing organizations. Several of the largest global manufacturing organizations have global, site licenses for STATISTICA, used throughout their manufacturing sites.
Applications range from Web-based monitoring of Quality Control to fairly standard statistical process control techniques to customized STATISTICA-based applications for analyses that are specific to the type of manufacturing being performed.
Capital equipment manufacturers typically provide basic and extended warranties to their customers as a value-added service. The length of warranty to provide and its associated cost for each product are important concerns for these organizations.
It is also helpful from product improvement and repair process improvement perspectives to be able to determine the most frequent repairs by product, the factors that contribute to a failure type, and the correlations between failures (e.g., if the repair technician determines that the water pump needs to be replaced, they may as well replace another component that is also likely to fail).
STATISTICA‘s data mining and text mining algorithms are critical components in the successful setting of warranty parameters and the determination of repair guidelines and rules to decrease warranty service costs.
As a value-added service to their customers, organizations are able to offer remote monitoring services to their customers that deploy data transmission devices on their products and feed data to a centralized database. STATISTICA is integrated with those databases and monitors the various data feeds from the customer’s equipment. For example, the STATISTICA application includes predictive models to monitor oil pressure, RPMs, water pressure and various other equipment parameters. STATISTICA provides automated alerting and exception reporting when the latest data predict a problem or a failure for a piece of equipment. The organization notifies the customer proactively before there is a problem and a decision is made about whether a repair technician should be sent out to make adjustments to the machine.
StatSoft’s customers in the Capital Equipment Industry use the broad base of analytic techniques in the platform to determine regional patterns in their sales and to make cross-selling and up-selling recommendations based upon what an individual customer just purchased, what they already own, the business that the customer is in, the region in which the customer is based, etc.
- Enterprise Resource Planning (ERP) systems
- Laboratory Information Management Systems (LIMS)
- Manufacturing Execution Systems (MES)
- Data historians (e.g., PI)
- Data warehouses
- Connectivity to SAP
STATISTICA Enterprise allows you maintain the connections to this data, and the analyses of the data, all in one centralized application.
The data sources supported by STATISTICA Enterprise include the following:
- Any OLE DB or ODBC relational database, such as Oracle, SQL Server, or Access
- Flat files like Excel spreadsheets and STATISTICA spreadsheets
- Data historian repositories such as the PI Data Historian from OSI Soft, Inc.
- In-Place Database Processing (IDP), query multidimensional databases containing terabytes of data and process data without importing to local storage
STATISTICA Enterprise also provides convenient tools for filtering your data, and for viewing the metadata associated with those data sources.
The STATISTICA Enterprise administration application (called the Enterprise Manager) allows you to specify multiple users, with passwords, and assign different roles to those different users. For example, your database administrators can be given permission to create and modify database connections and queries, and your engineers can be given permission to run those queries and analyze the resulting data. Furthermore, each engineer can be assigned permissions for only the analyses that pertain to his or her work.