All Of Statistica Models, Predictive, Stacking, Text Mining, Data Warehousing And On-Line Analytic Processing (Olap)

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.

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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

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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).

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.

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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.

Other methods for combining the prediction from multiple models or methods (e.g., from multiple datasets used for learning) are Boosting and Bagging (Voting).

TEXT MINING

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.

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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.).

DATA WAREHOUSING

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.

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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.

ON-LINE ANALYTIC PROCESSING (OLAP)

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.

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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.

 

Reference source : documentation(dot)statsoft(dot)com