Data Mining Tools


A large number of different kinds of tools are used in data mining, reflecting the eclecticism of its origins. Some recent ones in pattern recognition and detection, culled from the data mining literature with no particular objective other than to indicate the diversity of different kinds of method, are tools for characterizing, identifying, and locating patterns in multivariate response data; tools for detecting and identifying patterns in two-dimensional displays (such as fingerprints and meteorological charts); identifying sudden changes over time (such as those induced by chemical leakages or intrusion detection in computer systems);  detecting changes in behavior (animal migration routes; fraud detection is a common application in various domains, including commercial, scientific, and governmental – such as electoral fraud); and identifying logical combinations of values which differ between groups.

Some examples of important tools in model building in data mining (again chosen with no particular aim other than to illustrate the range of such methods) include recursive partitioning, cluster analysis, regression modeling, segmentation of time series into a small number of segment types, techniques for condensing huge (tens of billions of data points) data sets into manageable summaries, and collaborative filtering, in which transactions are processed as they arrive so that future transactions may be treated in a more appropriate manner.

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