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HUOPM: High-Utility Occupancy Pattern Mining.

Authors
  • Gan, Wensheng
  • Lin, Jerry Chun-Wei
  • Fournier-Viger, Philippe
  • Chao, Han-Chieh
  • Yu, Philip S
Type
Published Article
Journal
IEEE Transactions on Cybernetics
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Mar 01, 2020
Volume
50
Issue
3
Pages
1195–1208
Identifiers
DOI: 10.1109/TCYB.2019.2896267
PMID: 30794524
Source
Medline
Language
English
License
Unknown

Abstract

Mining useful patterns from varied types of databases is an important research topic, which has many real-life applications. Most studies have considered the frequency as sole interestingness measure to identify high-quality patterns. However, each object is different in nature. The relative importance of objects is not equal, in terms of criteria, such as the utility, risk, or interest. Besides, another limitation of frequent patterns is that they generally have a low occupancy, that is, they often represent small sets of items in transactions containing many items and, thus, may not be truly representative of these transactions. To extract high-quality patterns in real-life applications, this paper extends the occupancy measure to also assess the utility of patterns in transaction databases. We propose an efficient algorithm named high-utility occupancy pattern mining (HUOPM). It considers user preferences in terms of frequency, utility, and occupancy. A novel frequency-utility tree and two compact data structures, called the utility-occupancy list and frequency-utility table, are designed to provide global and partial downward closure properties for pruning the search space. The proposed method can efficiently discover the complete set of high-quality patterns without candidate generation. Extensive experiments have been conducted on several datasets to evaluate the effectiveness and efficiency of the proposed algorithm. Results show that the derived patterns are intelligible, reasonable, and acceptable, and that HUOPM with its pruning strategies outperforms the state-of-the-art algorithm, in terms of runtime and search space, respectively.

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