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Mining Cross-Level Closed Sequential Patterns

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10933))

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Abstract

Multilevel, cross-level and sequential knowledge plays a significant role in our several real-life aspects including market basket analysis, bioinformatics, texts mining etc. Many researchers have proposed various approaches for mining hierarchical patterns. However, some of the existing approaches generate many multilevel and cross-level frequent patterns that fail to fetch quality information. It is extremely difficult to extract any meaningful information from these large number of redundant patterns. There exist some approaches that mines multilevel and cross-level closed patterns but unfortunately, there is no cross-level closed pattern mining method proposed yet which maintain the sequence of itemsets. In this paper, we develop an algorithm, called CCSP (Cross-level Closed Sequential Pattern mining) to conduct cross-level hierarchical patterns that provide maximal information. Our work has made contributions in mining patterns, which express the mixed relationship between the generalized and specialized view of the transaction itemsets. We have extensively evaluated our proposed algorithm’s efficiency using a variety of real-life datasets and performing a large number of experiments.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh

    Rutba Aman & Chowdhury Farhan Ahmed

Authors
  1. Rutba Aman
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  2. Chowdhury Farhan Ahmed
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Correspondence to Rutba Aman .

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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Aman, R., Ahmed, C.F. (2018). Mining Cross-Level Closed Sequential Patterns. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2018. Lecture Notes in Computer Science(), vol 10933. Springer, Cham. https://doi.org/10.1007/978-3-319-95786-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-95786-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95785-2

  • Online ISBN: 978-3-319-95786-9

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