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H-Mine: Fast and space-preserving frequent pattern mining in large databases

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In this study, we propose a simple and novel data structure using hyper-links, 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 a very limited and precisely predictable main memory cost and runs very quickly in memory-based settings. Moreover, it can be scaled up to very large databases using database partitioning. 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 an excellent performance for various kinds of data, outperforms currently available algorithms in different settings, and is highly scalable to mining large databases. This study also proposes a new data mining methodology, space-preserving mining, which may have a major impact on the future development of efficient and scalable data mining methods.
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Keywords: FP-tree; Frequent pattern mining; transaction databases

Document Type: Research Article

Affiliations: 1: School of Computing Science, Simon Fraser University, Burnaby, BC, Canada 2: University of Illinois, Urbana, IL, USA 3: Hong Kong University of Science and Technology, Hong Kong 4: Osaka University, Osaka, Japan 5: Peking University, Beijing, China

Publication date: 01 June 2007

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