A framework for evaluating knowledge-based interestingness of association rules

被引：4

作者：

Shekar B. ^{[1
]}

Natarajan R. ^{[1
]}

机构：

[1] Quantit. Methods/Info. Systems Area, Indian Inst. of Management Bangalore

来源：

Fuzzy Optimization and Decision Making | 2004年 / 3卷 / 02期

关键词：

Association rules; Fuzzy taxonomy; Interestingness; Item-relatedness;

D O I：

10.1023/B:FODM.0000022043.43885.55

中图分类号：

学科分类号：

摘要：

In Knowledge Discovery in Databases (KDD)/Data Mining literature, "interestingness" measures are used to rank rules according to the "interest" a particular rule is expected to evoke. In this paper, we introduce an aspect of subjective interestingness called "item- relatedness". Relatedness is a consequence of relationships that exist between items in a domain. Association rules containing unrelated or weakly related items are interesting since the co-occurrence of such items is unexpected. 'Item-Relatedness' helps in ranking association rules on the basis of one kind of subjective unexpectedness. We identify three types of item-relatedness - captured in the structure of a "fuzzy taxonomy" (an extension of the classical concept hierarchy tree). An "item- relatedness" measure for describing relatedness between two items is developed by combining these three types. Efficacy of this measure is illustrated with the help of a sample taxonomy. We discuss three mechanisms for extending this measure from a two-item set to an association rule consisting of a set of more than two items. These mechanisms utilize the relatedness of item-pairs and other aspects of an association rule, namely its structure, distribution of items and item-pairs. We compare our approach with another method from recent literature.

引用

页码：157 / 185

页数：28

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