Author(s)

D. R. Carvalho, A. A. Freitas & N. F. F. Ebecken

Abstract

A critical review of rule surprisingness measures D. R. ~arvalho"~, A. A. re it as^ & N. F. F. beck en' I COPPE / Universidade Federal do Rio de Janeiro, Brasil 2 University of Kent at Canterbury, UK 3 Universidade Tuiuti do Paranci, Brasil Abstract In data mining it is usually desirable that discovered knowledge have some characteristics such as being as accurate as possible, comprehensible and surprising to the user. The vast majority of data mining algorithms produce, as part of their results, information of a statistical nature that allows the user to assess how accurate and reliable the discovered knowledge is. However, in many cases this is not enough for the user. Even if discovered knowledge is highly accurate from a statistical point of view, it might not be interesting for the user. Few data mining algorithms produce, as part of their results, a measure of the degree of surprisingness of discovered knowledge. However, these measures can be computed in a post-processing phase, as a fo

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