Item Sets That Compress

Abstract

One of the major problems in frequent item set mining is the explosion of the number of results: it is difficult to find the most interesting frequent item sets. The cause of this explosion is that large sets of frequent item sets describe essentially the same set of transactions. In this paper we approach this problem using the MDL principle: the best set of frequent item sets is that set that compresses the database best. We introduce four heuristic algorithms for this task, and the experiments show that these algorithms give a dramatic reduction in the number of frequent item sets. Moreover, we show how our approach can be used to determine the best value for the min-sup threshold.

Mining Databases to Mine Queries Faster

“…In [15] we defined the optimal set of (frequent) item sets as that one whose associated code table minimises the total compressed size:…”

“…Rather, in this paper we study the problem for one specific class of models, viz., the code tables induced by our Krimp algorithm [15]. Given all frequent item sets on a table, Krimp selects a small subset of these frequent item sets.…”

“…For the convenience of the reader we provide a brief introduction to Krimp in this section, it was originally introduced in [15] (although not by that name) and the reader is referred to that paper for more details.…”

Identifying the components

“…In Siebes et al (2006) we introduced the krimp algorithm. 1 This MDL-based algorithm picks a few descriptive frequent item sets that compress the data well.…”

Hierarchical, Parameter-Free Community Discovery

“…Keogh et al [17] developed a simple and effective scheme for mining time-series data through compression. Actually, compression or Minimum Description Language (MDL) have become the workhorse of many parameter-free algorithms: frequent itemsets [24], biclustering [4,23], time-evolving graph clustering [25], and spatial-clustering [20].…”