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Finding Top-k Fuzzy Frequent Itemsets from Databases

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Book cover Data Mining and Big Data (DMBD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

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Abstract

Frequent itemset mining is an important in data mining. Fuzzy data mining can more accurately describe the mining results in frequent itemset mining. Nevertheless, frequent itemsets are redundant for the users. A better way is to show the top-k results accordingly. In this paper, we define the score of fuzzy frequent itemset and propose the problem of top-k fuzzy frequent itemset mining, which, to the best of our knowledge, has never been focused on before. To address this problem, we employ a data structure named TopKFFITree to store the superset of the mining results, which has a significantly reduced size in comparison to all the fuzzy frequent itemsets. Then, we present an algorithm named TopK-FFI to build and maintain the data structure. In this algorithm, we employ a method to prune most of the fuzzy frequent itemsets immediately based on the monotony of itemset score. Theoretical analysis and experimental studies over 4 datasets demonstrate that our proposed algorithm can efficiently decrease the runtime and memory cost, and significantly outperform the naive algorithm Top-k-FFI-Miner.

This research is supported by the National Natural Science Foundation of China(61100112,61309030), Beijing Higher Education Young Elite Teacher Project(YETP0987), State Key Program of the National Social Science Foundation of China(13AXW010), Discipline Construction Foundation of Central University of Finance and Economics(2016XX05).

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Authors and Affiliations

  1. School of Information, Central University of Finance and Economics, Beijing, China

    Haifeng Li, Yue Wang, Ning Zhang & Yuejin Zhang

Authors
  1. Haifeng Li
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  2. Yue Wang
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  3. Ning Zhang
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  4. Yuejin Zhang
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Corresponding author

Correspondence to Haifeng Li .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Li, H., Wang, Y., Zhang, N., Zhang, Y. (2017). Finding Top-k Fuzzy Frequent Itemsets from Databases. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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