A Survey Paper on a Compact Data Structure Based Technique for Mining Frequent Closed Item Set

Abstract

Association discovery finds closely correlate sets so the presence of some components in an exceedingly frequent set can imply the presence of the remaining components (in identical set). Closed item sets are a solution to the problems described above. These are obtained by partitioning the lattice of frequent item sets into equivalence classes according to the following property: two distinct item sets belong the same class if and only if they occur in the same set of transactions. Closed item sets are the collection of maximal item sets of these equivalence classes. This paper proposes a comprehensive survey of the closed item set mining. The concept of the frequent closed item set mining is also elaborated in detail. The modern methods of frequent closed item set mining are also discussed in brief.