H-mine: hyper-structure mining of frequent patterns in large databases

By Jian Pei, Jiawei Han, Hongjun Lu, Shojiro Nishio, Shiwei Tang, Dongqing Yang. Proceedings 2001 IEEE International Conference on Data Mining, 2001

525 citations recorded by the source corpus.

Methods for efficient mining of frequent patterns have been studied extensively by many researchers. However, the previously proposed methods still encounter some performance bottlenecks when mining databases with different data characteristics, such as dense vs. sparse, long vs. short patterns, memory-based vs. disk-based, etc. In this study, we propose a simple and novel hyper-linked data structure, H-struct and a new mining algorithm, H-mine, which takes advantage of this data structure and dynamically adjusts links in the mining process. A distinct feature of this method is that it has very limited and precisely predictable space overhead and runs really fast in memory-based setting. Moreover it can be scaled up to very large databases by database partitioning, and when the data set becomes dense, (conditional) FP-trees can be constructed dynamically as part of the mining process. Our study shows that H-mine has high performance in various kinds of data, outperforms the previously developed algorithms in different settings, and is highly scalable in mining large databases. This study also proposes a new data mining methodology, space-preserving mining, which may have strong impact in the future development of efficient and scalable data mining methods.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/22fcc2d2aeb5707498d144e80f926b1ea3bed6d5

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Citations in the corpus

A Survey of Sequential Pattern Mining (2017, methodology): “In the PreﬁxSpan algorithm an optimization called pseudo-projection is used to reduce this cost, which consists of implementing a projected database as a set of pointers on the original database [89, 86].”
Observation of sales trends by mining emerging patterns in dynamic markets (2018, methodology): “These experimental results are same as the Apriori algorithm [3], FP-growth [18], and H-mine [30].”
CLOSET+: searching for the best strategies for mining frequent closed itemsets (2003, unknown): “Most of the well studied frequent pattern mining algorithms, including Apriori [2], FP-growth [8], H-mine [13], and OP [10], mine the complete set of frequent itemsets.”