Interestingness measures for data mining: A survey

Liqiang Geng

doi:10.1145/1132960.1132963

2006-09-30

706

PRE

Liqiang Geng *

Howard J. Hamilton

DOI：10.1145/1132960.1132963

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摘要

En 中文

Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined. These measures are intended for selecting and ranking patterns according to their potential interest to the user. Good measures also allow the time and space costs of the mining process to be reduced. This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives strategies for selecting appropriate measures for applications, and identifies opportunities for future research in this area.

Keyword:

algorithms

measurement

knowledge discovery

classification rules

interestingness measures

interest measures

summaries

association rules

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