FIMI’03: Workshop on Frequent Itemset Mining Implementations

Advances in Frequent Itemset Mining Implementations: Report on FIMI’03 FIMI Repository: http://fimi.cs.helsinki.fi/

nonordfp: An FP-growth variation without rebuilding the FP-tree

We describe a frequent itemset mining algorithm and implementation based on the well-known algorithm FPgrowth. The theoretical difference is the main data structure (tree), which is more compact and which we do not need to rebuild for each conditional step. We thoroughly deal with implementation issues, data structures, memory layout, I/O and library functions we use to achieve comparable perfo...

Improving Direct Counting for Frequent Itemset Mining

During the last ten years, many algorithms have been proposed to mine frequent itemsets. In order to fairly evaluate their behavior, the IEEE/ICDM Workshop on Frequent Itemset Mining Implementations (FIMI’03) has been recently organized. According to its analysis, kDCI++ is a state-of-the-art algorithm. However, it can be observed from the FIMI’03 experiments that its efficient behavior does no...

Proceedings of FIMI'03 Workshop on Frequent Itemset Mining Implementations

The efficiency of frequent itemset mining algorithms is determined mainly by three factors: the way candidates are generated, the data structure that is used and the implementation details. Most papers focus on the first factor, some describe the underlying data structures, but implementation details are almost always neglected. In this paper we show that the effect of implementation can be mor...

On compressing frequent patterns q

A major challenge in frequent-pattern mining is the sheer size of its mining results. To compress the frequent patterns, we propose to cluster frequent patterns with a tightness measure d (called d-cluster), and select a representative pattern for each cluster. The problem of finding a minimum set of representative patterns is shown NP-Hard. We develop two greedy methods, RPglobal and RPlocal. ...