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Rhythmus periodic frequent pattern mining without periodicity threshold

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Abstract

Periodic frequent pattern mining (PFPM) with temporal regularity is an emerging field in data mining. The user-defined maximum periodicity threshold restricts most of the existing PFPM approaches in mining valuable patterns. The scenario is not good for real world applications. Even a pattern having all the periods below the threshold value except the one becomes irregular. In addition, the patterns with all the periods higher than the threshold value are out of consideration. Hence, there is a high chance of missing out valuable patterns in the existing approaches. To solve the problem, this paper introduces rhythmus period (RP) for PFPM. In real life applications, patterns occur with uneven temporal intervals or periods. However, uneven periods of a pattern often display some sort of consistency. Interestingly, occurrence of a pattern with consistent periods also exhibit high regularity in the database. The periods of a pattern that follow some sort of consistency are called rhythmus periods. Understanding the rhythmus periods of the patterns is of high interest in the fields of targeted marketing, targeted advertisement, intrusion detection etc. This paper introduces a new interestingness measure called rhythmus period ratio (RPR) based on the rhythmus periods of the patterns. Accordingly, a new approach termed as rhythmus periodic frequent pattern mining (RPFPM) is proposed for discovering the periodic frequent patterns without periodicity threshold. The outcomes of the comprehensive experiments on both of the synthetic and real world datasets show the effectiveness of the proposed approach.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Nadia, West Bengal, India

    Subrata Datta & Kalyani Mali

  2. Tata Consultancy Services Ltd., Kolkata, West Bengal, India

    Sourav Das

  3. Department of Computer Science and Engineering, Neotia Institute of Technology, Management and Science, Sarisha, West Bengal, India

    Subrata Datta, Srijita Kundu & Sayanta Harh

Authors
  1. Subrata Datta
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  2. Kalyani Mali
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  3. Sourav Das
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  4. Srijita Kundu
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  5. Sayanta Harh
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Contributions

SD was involved in developing the concept, designing the algorithms and wrote the manuscript. KM was involved in supervising the experiments and resource sharing. SD was involved in coding the algorithms. SK was involved in experimental analysis. SH was involved in data analysis. All of the authors have read and approved the final manuscript.

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Correspondence to Subrata Datta.

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Datta, S., Mali, K., Das, S. et al. Rhythmus periodic frequent pattern mining without periodicity threshold. J Ambient Intell Human Comput 14, 8551–8563 (2023). https://doi.org/10.1007/s12652-021-03617-8

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  • DOI: https://doi.org/10.1007/s12652-021-03617-8

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