Background and Related Work

Abstract

In KDD, the knowledge that we seek to discover describes patterns in the data as opposed to knowledge about the data itself. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, sequential patterns, time series, contingency tables, summaries obtained using some hierarchical or taxonomic structure, and others. Typically, the number of patterns generated is very large, but only a few of these patterns are likely to be of any interest to the domain expert analyzing the data. The reason for this is that many of the patterns are either irrelevant or obvious, and do not provide new knowledge [105]. To increase the utility, relevance, and usefulness of the discovered patterns, techniques are required to reduce the number of patterns that need to be considered. Techniques which satisfy this goal are broadly referred to as interestingness measures.