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VRKSHA: a novel tree structure for time-profiled temporal association mining

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Abstract

Mining association patterns from a time-stamped temporal database distributed over finite time slots is implicitly associated with task of scanning the input database. Finding supports of itemsets requires scanning the input database. Database scan can be performed in either snapshot or lattice-based approach. Sequential and SPAMINE methods for similarity-profiled association pattern mining originally proposed by Jin Soung Yoo and Sashi Sekhar are based on the snapshot database scan and lattice scan, respectively. Snapshot database scan involves scanning multi-time slot database time slot by time slot. The major limitation of Sequential method is the requirement to retain original temporal database in the disk for finding itemset support computations. In this paper, a novel multi-tree structure called VRKSHA is proposed that eliminates the need to store the original temporal database in the memory and also eliminates the need to retain database in memory. The basic idea is to generate a compressed time-stamped temporal tree and use this multi-tree structure to obtain true supports of temporal itemsets for a given time slot. Discovery of similar temporal itemsets is based on finding distance between temporal itemset and reference w.r.t each time slot and validating whether the computed distance satisfies specified user dissimilarity threshold. A pattern is pruned if the dissimilarity condition fails at any given time slot well before computing true support of itemset w.r.t all time slots. The advantage of proposed Sequential approach is from the fact that it is a single database scan approach excluding the initial database scan performed for computing true supports of singleton items. VRKSHA overcomes the major limitation of retaining database in memory that is required by SPAMINE, G-SPAMINE, MASTER algorithms. Experiment results prove that computational time and memory consumed by VRKSHA are significantly very much better than by approaches such as Naïve, Sequential, SPAMINE, and G-SPAMINE. To the best of our survey and knowledge, VRKSHA is the pioneering work to introduce and propose a compressed tree-based data structure for mining similarity-profiled temporal association patterns in the area of time-profiled temporal association mining.

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Authors and Affiliations

  1. Jordan University of Science and Technology, Ar Ramtha, Jordan

    Shadi A. Aljawarneh

  2. Department of Information Technology, VNR VJIET, Hyderabad, India

    V. Radhakrishna & Aravind Cheruvu

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  1. Shadi A. Aljawarneh
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  2. V. Radhakrishna
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  3. Aravind Cheruvu
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Correspondence to Shadi A. Aljawarneh.

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Aljawarneh, S.A., Radhakrishna, V. & Cheruvu, A. VRKSHA: a novel tree structure for time-profiled temporal association mining. Neural Comput & Applic 32, 16337–16365 (2020). https://doi.org/10.1007/s00521-018-3776-7

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