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More Unique Features of STATISTICA Data Miner

STATISTICA Data Miner contains a large number of fully integrated advanced techniques for analyzing data. In addition, the architecture of the program allows this software to offer features that are absolutely unique in this type of application, and can be crucial for the success of data mining projects in the real world.

The most comprehensive selection of data mining techniques. To the best of our knowledge, STATISTICA Data Miner contains the most comprehensive selection of data mining methods available on the market (e.g., by far the most comprehensive selection of clustering techniques, neural networks architectures, classification/regression trees, multivariate modeling (including MARSplines), and many other predictive techniques; the largest selection of graphics and visualization procedures of any competing products).

A fully integrated STATISTICA application. STATISTICA Data Miner is fully integrated into the STATISTICA line of desktop and Web-based analytic software (WebSTATISTICA): Everything works together seamlessly as a single, comprehensive system.

Seamless integration of a vast range of techniques. The seamless integration of STATISTICA Data Miner with all other analytic and graphics options available in STATISTICA provides unmatched flexibility: for example, no other software will allow you to quickly integrate into a single data mining project quality control charting and SixSigma methods, trained ensembles of multiple-architecture neural networks providing a weighted average predictions, and categorized icon charts summarizing multiple features of interest for each observation. In STATISTICA Data Miner, all of these can be connected by dragging the respective analysis nodes into the data mining workspace.

Every result can further be reviewed, analyzed, saved. All results of STATISTICA Data Miner can be displayed in the same manner as the results from other STATISTICA analyses. Hence, intermediate results can be saved or immediately used to perform additional interactive analyses using the standard STATISTICA interactive user interface; there are no files to import or export. For example, just display the spreadsheet with predictions and instantly use that spreadsheet to review graphically whether any outliers might have influenced the results.

Analysis nodes will handle multiple data streams. Because of STATISTICA Data Miner's unique architecture (see Advanced Software Technology), multiple data streams can be channeled through a single node: for example, you can specify a single node for clustering, and send 20 data sets with different variable selections through that node, applying identical specifications such as the type of distance measure to use, etc. This allows for efficient processing of lists of data sources (e.g., automatically create identical analyses and reports for data collected from different data processing centers).

In-place processing of large data sets on remote servers. STATISTICA includes advanced options for defining connections to databases in practically all formats on remote servers. To the STATISTICA application, these data sources appear just as another data file that can be processed without the need to make a copy of or "import" the database to the local machine. Because STATISTICA Data Miner is just another seamlessly integrated STATISTICA application, those data sources can be connected like any other data source, i.e., by simply selecting it from a list of available input data. STATISTICA Data Miner also includes special options for selecting subsets of variables from among huge numbers of input variables (feature selection, variable filtering). For example, you can scan over a million of input variables for candidate variables for further predictive classification analyses.

Open architecture: Add your own custom nodes. Because all nodes (including any new, custom-Nodes) in STATISTICA Data Miner can be modified via Visual Basic programs, it is very easy to customize the system to include analysis (or other) nodes that (a) contain your own proprietary algorithms, (b) developed and implemented in any language that can generate functions that can be called from industry-standard Visual Basic, (c) with a complete user interface for accepting from the user parameters, choices of options, etc.; these nodes can be added permanently to the selection of available nodes, and identified with an icon containing your custom logo.

Same user interface: Data mining on your local machine or via WebSTATISTICA. The same user interface and options available in the STATISTICA Data Miner desktop application are available in the WebSTATISTICA Data Miner application. To reiterate, STATISTICA Data Miner is fully integrated into the STATISTICA family of products; it is not a "foreign" application developed by another company and "forced" into the STATISTICA framework. Data mining over the Web (via WebSTATISTICA) is as (or more) efficient and convenient as it is within the STATISTICA desktop application. Note that the WebSTATISTICA Client-Server installation of STATISTICA Data Miner offers additional advantages for processing very large datasets: The program will automatically take advantage of multi-processor and/or multiple-server architectures (with proper hardware support), to evaluate models via multiple simultaneous processes (multithreading, distributed processing).

STATISTICA Data Miner is itself accessible as a COM object. The functions of STATISTICA Data Miner are also fully integrated and accessible via the STATISTICA COM object model, and they can be called from other applications or used in analysis macros (e.g., create predictions from a sophisticated trained multi-architecture model by clicking on a toolbar button). IT departments will be able to create very simple STATISTICA - based applications that can be used by "operators" (e.g., loan officers reviewing credit applications for fraudulent information) who simply click on predefined buttons; yet the system may utilize the "wisdom" extracted from testing dozens or even hundreds of different methods for prediction.

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