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A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns

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Abstract

Mining temporal association patterns from time-stamped temporal databases, first introduced in 2009, remain an active area of research. A pattern is temporally similar when it satisfies certain specified subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns in the context of temporal databases. The brute force approach requires performing \(2^{n }\) true support computations for ‘n’ items; hence, an NP-class problem. Also, the apriori or fp-tree-based algorithms designed for static databases are not directly extendable to temporal databases to retrieve temporal patterns similar to a reference prevalence of user interest. This is because the support of patterns violates the monotonicity property in temporal databases. In our case, support is a vector of values and not a single value. In this paper, we present a novel approach to retrieve temporal association patterns whose prevalence values are similar to those of the user specified reference. This allows us to significantly reduce support computations by defining novel expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. We then introduce a novel dissimilarity measure, which is the fuzzy Gaussian-based dissimilarity measure. The measure also holds the monotonicity property. Our evaluations demonstrate that the proposed method outperforms brute force and sequential approaches. We also compare the performance of the proposed approach with the SPAMINE which uses the Euclidean measure. The proposed approach uses monotonicity property to prune temporal patterns without computing unnecessary true supports and distances.

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Authors and Affiliations

  1. Department of IT, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India

    Vangipuram Radhakrishna

  2. Department of Software Engineering, Jordan University of Science and Technology, Irbid, Jordan

    Shadi A. Aljawarneh

  3. Department of CSE, University College of Engineering, Osmania University, Hyderabad, India

    Puligadda Veereswara Kumar

  4. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  5. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Kim-Kwang Raymond Choo

Authors
  1. Vangipuram Radhakrishna
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  2. Shadi A. Aljawarneh
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  3. Puligadda Veereswara Kumar
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  4. Kim-Kwang Raymond Choo
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Corresponding author

Correspondence to Kim-Kwang Raymond Choo.

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All authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Radhakrishna, V., Aljawarneh, S.A., Kumar, P.V. et al. A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns. Soft Comput 22, 1903–1919 (2018). https://doi.org/10.1007/s00500-016-2445-y

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