Issue No. 06 - December (1996 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/69.553165
<p><b>Abstract</b>—One of the central problems in the field of knowledge discovery is the development of good measures of interestingness of discovered patterns. Such measures of interestingness are divided into <it>objective</it> measures—those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the <it>subjective</it> measures—those that also depend on the class of users who examine the pattern. The focus of this paper is on studying subjective measures of interestingness. These measures are classified into <it>actionable</it> and <it>unexpected</it>, and the relationship between them is examined. The unexpected measure of interestingness is defined in terms of the <it>belief</it> system that the user has. Interestingness of a pattern is expressed in terms of how it affects the belief system. The paper also discusses how this unexpected measure of interestingness can be used in the discovery process.</p>
Measures of interestingness, patterns, actionability, unexpectedness, belief systems.
Alexander Tuzhilin, Avi Silberschatz, "What Makes Patterns Interesting in Knowledge Discovery Systems", IEEE Transactions on Knowledge & Data Engineering, vol. 8, no. , pp. 970-974, December 1996, doi:10.1109/69.553165