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On Explanation-Oriented Data Mining

INTRODUCTION Data mining concerns theories, methodologies, and in particular, computer systems for knowledge extraction or mining from large amounts of data (Han, 2000). The extensive studies on data mining have led to many theories, methodologies, efficient algorithms and tools for the discovery of different kinds of knowledge from different types of data. In spite of their differences, they share the same goal, namely, to discover the new and useful knowledge, in order to gain a better understanding of nature. The objective of data mining is in fact the goal of scientists when carrying out scientific research, independent of their various disciplines. Data mining, combining research methods and computer technology, by its nature, should be considered as a research support system. This goaloriented view enables us to re-examine data mining in a wider context of scientific research. Such a re-examination leads the new insights into data mining and knowledge discovery. The consequence after the immediate comparison between scientific research and data mining is that an explanation construction and evaluation task is added to the existing data mining framework. In this chapter, we elaborate the basic concerns and methods of explanation construction and evaluation. Explanation-oriented association mining is employed as a concrete example to show the whole framework.