Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Interesting Fuzzy Association Rules in Quantitative Databases
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Graph Theory with Applications to Engineering and Computer Science (Prentice Hall Series in Automatic Computation)
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'Interestingness' measures are used to rank rules according to the 'interest' a particular rule is expected to evoke in a user. In this paper, we introduce an aspect of interestingness called 'item-relatedness' to determine interestingness of item-pairs occurring in association rules. We elucidate and quantify three different types of item-relatedness. Relationships corresponding to item-relatedness proposed by us are shown to be captured by paths in a 'fuzzy taxonomy' (an extension of the concept hierarchy tree). We then combine these measures of item-relatedness to arrive at a total-relatedness measure. We finally demonstrate the efficacy of this total measure on a sample taxonomy.