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Mining top-k frequent patterns with combination reducing techniques

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Abstract

Top-k frequent pattern mining finds interesting patterns from the highest support to the k-th support. The approach can be effectively applied in numerous fields such as marketing, finance, bio-data analysis, and so on since it does not need constraints by a minimum support threshold. Top-k mining methods use the support of the k-th pattern, not a user-specified minimum support. Thus, the methods conduct mining operations based on very low supports until the k-th pattern is detected. When a low support is used in the mining process, single-paths with numerous items are generated, where the top-k mining algorithm extracts valid patterns by combining the items for each single-path. Therefore, the bigger the number of combinations is, the larger the increase in time and memory consumption is. In this paper, in order to mine top-k frequent patterns more efficiently, we consider converting patterns obtained from single-paths into composite patterns during the mining process and recovering them as the original patterns when the top-k frequent patterns are extracted. For this, we define a new concept, the composite pattern, and propose novel techniques for reducing pattern combinations in the single-path. Two algorithms are introduced in this paper, where the former is CRM (Combination Reducing method), applying our reduction manner, and the latter is CRMN (Combination Reducing method for N-itemset), considering N-itemset, i.e., patterns’ lengths. A performance evaluation shows that CRM and CRMN algorithms can efficiently reduce pattern combinations in single-paths compared to state-of-the-art algorithms. The experimental results also illustrate that our approaches have outstanding performance in terms of runtime, memory, and scalability.

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Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2013005682 and 20080062611).

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  1. Department of Computer Engineering, Sejong University, Seoul, Korea

    Gwangbum Pyun & Unil Yun

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  1. Gwangbum Pyun
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  2. Unil Yun
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Correspondence to Unil Yun.

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Pyun, G., Yun, U. Mining top-k frequent patterns with combination reducing techniques. Appl Intell 41, 76–98 (2014). https://doi.org/10.1007/s10489-013-0506-9

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