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Discovering Erasable Closed Patterns

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Intelligent Information and Database Systems (ACIIDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9011))

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Abstract

Data mining that discovers knowledge from large datasets is more and more popular in artificial intelligence. In recent years, the problem of mining erasable patterns (EPs) has been proposed as an interesting variant of frequent pattern mining. There are many algorithms for solving effectively the problem of mining EPs. However, for very big datasets, the large number of EPs takes the large memory usage of the system, and then obstructs users’ using the system. Therefore, it is necessary to mine a condensed representation of EPs. In this paper, we present the erasable closed patterns (ECPs) concept and an effective algorithm for mining ECPs (MECP algorithm). The experimental results show that the number of ECPs is much less than that of EPs. Besides, the runtime of MECP is better than the naïve approach for mining ECPs.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Giang Nguyen & Bay Vo

  2. Division of Data Science and Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Tuong Le

  3. Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam

    Bac Le

Authors
  1. Giang Nguyen
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  2. Tuong Le
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  3. Bay Vo
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  4. Bac Le
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Corresponding author

Correspondence to Tuong Le .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh city, Vietnam

    Ngoc Thanh Nguyen

  2. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

  3. Bina Nusantara University, Jakarta, Indonesia

    Raymond Kosala

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© 2015 Springer International Publishing Switzerland

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Nguyen, G., Le, T., Vo, B., Le, B. (2015). Discovering Erasable Closed Patterns. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_36

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  • DOI: https://doi.org/10.1007/978-3-319-15702-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15701-6

  • Online ISBN: 978-3-319-15702-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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