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The Technology Tree
The top level dichotomization of the data mining technologies can be based on the retention of data; that is, do we still keep or need the data after we have mined it? In most cases, not. However, in some early approaches much of the data set was still maintained for future pattern matching. Obviously, these retention-based techniques only apply to the tasks of predictive modeling and forensic analysis, and not knowledge discovery since they do not distill any patterns.
As one would expect, approaches based on data retention quickly run into problems because of large data sets. However, in some cases predictiveresults can be obtained with these techniques and for the sake of completeness I briefly review them in the next section.
Figure 2.
As shown in Figure 2, approaches based on pattern distillation fall into three categories: logical, cross-tabulational and equational. I will review eachof these and their sub-branches separately. Each leaf of the tree in Figure 2 shows a distinct method of implementing a system based on a technique (e.g., several types of decision tree algorithms).
Not all approaches based on pattern distillation provide knowledge, since the patterns may be distilled into an "opaque" language or formalism not easily readable by humans such as very complex equations. Hence, some of these approaches produce "transparent" and understandable patterns of knowledge, others just produce patterns used for opaque prediction.
Copyright (C) 1997, Journal of Data Warehousing, December 1997 |
