What Is Interesting: Studies on Interestingness in Knowledge Discovery

Knowledge Discovery in Databases (KDD) was defined by [FPSS96a] as “[. . . ] the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data.” As the size of databases increases, the number of patterns mined from them also increases. This number can easily increase to an extent that overwhelms users. To address this problem, patterns need to be processed for interestingness in order to distinguish the “valid, novel, potentially useful and ultimately understandable patterns” from those that are not. Interestingness has been identified as a central problem in KDD. In this work, we study this problem: the interestingness problem. We start with an empirical evaluation of objective interestingness ranking criteria (in Chapter 3 based on [SM99]). Interestingness is ultimately subjective, and so we go on to introduce a new approach to subjective interestingness (in Chapter 4, based on [Sah99]). This approach is different from the two prior subjective interestingness approaches in that it uses very simple and very little domain knowledge—that is easy to acquire—to quickly eliminate the majority of the rules that are not interesting. We investigate how this approach can be incorporated into the mining process (in Chapter 5, based on [Sah02b]). To automate much of the tedious manual labor normally involved in interestingness exploration, we introduce a clustering framework for association rules, providing a data-driven grouping and naming scheme for the mined rules (in Chapter 6, based on [Sah02a]). The problem of interestingness is a notoriously difficult one. To reduce the size of the problem we introduce a new type of interestingness criteria, the Impartial Interestingness Criteria, as the first step in of the interestingness framework, the Interestingness PreProcessing Step (in Chapter 7, based on [Sah01]). The impartial interestingness criteria can be applied automatically to the output of any association mining algorithm, independently of its domain, task and users, to eliminate a significant portion of not-interesting rules.

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