Evaluation of Interestingness Measures for Ranking Discovered Knowledge
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
The Lorenz Dominance Order as a Measure of Interestingness in KDD
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
On Mining Summaries by Objective Measures of Interestingness
Machine Learning
Measuring the interestingness of discovered knowledge: A principled approach
Intelligent Data Analysis
The persuasive phase of visualization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Generating qualified summarization answers using fuzzy concept hierarchies
Proceedings of the 2010 Symposium on Information and Communication Technology
Data summarization for network traffic monitoring
Journal of Network and Computer Applications
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Abstract: An important problem in the area of data mining is the development of effective measures of interestingness for ranking discovered knowledge. The authors propose five principles that any measure must satisfy to be considered useful for ranking the interestingness of summaries generated from databases. We investigate the problem within the context of summarizing a single dataset which can be generalized in many different ways and to many levels of granularity. We perform a comparative sensitivity analysis of fifteen well-known diversity measures to identify those which satisfy the proposed principles. The fifteen diversity measures have previously been utilized in various disciplines, such as information theory, statistics, ecology, and economics. Their use as objective measures of interestingness for ranking summaries generated from databases is novel. The objective of this work is to gain some insight into the behaviour that can be expected from each of the diversity measures in practice, and to begin to develop a theory of interestingness against which the utility of new measures can be assessed.