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Information Journal Paper

Title

Fuzzy Sequential Pattern Mining over Quantitative Streams

Pages

  36-44

Abstract

 Sequential pattern mining is an interesting data mining problem with many real-world applications. Though new applications introduce a new form of data called Data Stream, no study has been reported on mining sequential patterns from the quantitative Data Stream. This paper presents a novel algorithm, for mining quantitative streams. The proposed algorithm can mine exact set of fuzzy sequential patterns in Sliding Window and Gap Constraints entailing the most recent transactions in a Data Stream. In addition, the proposed algorithm can also mine non-quantitative or transaction-based sequential patterns over a Data Stream. Numerical results show the running time and the memory usage of the proposed algorithm in the case of quantitative and customer-transaction-based sequence counting are proportional to the size of the Sliding Window and Gap Constraints.

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    • Cite

    APA: Copy

    Shakeri, Omid, Kelarestaghi, Manoochehr, Eshghi, Farshad, & Ganjtabesh, Ahmad. (2019). Fuzzy Sequential Pattern Mining over Quantitative Streams. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, 11(1), 36-44. SID. https://sid.ir/paper/770563/en

    Vancouver: Copy

    Shakeri Omid, Kelarestaghi Manoochehr, Eshghi Farshad, Ganjtabesh Ahmad. Fuzzy Sequential Pattern Mining over Quantitative Streams. INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH[Internet]. 2019;11(1):36-44. Available from: https://sid.ir/paper/770563/en

    IEEE: Copy

    Omid Shakeri, Manoochehr Kelarestaghi, Farshad Eshghi, and Ahmad Ganjtabesh, “Fuzzy Sequential Pattern Mining over Quantitative Streams,” INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH, vol. 11, no. 1, pp. 36–44, 2019, [Online]. Available: https://sid.ir/paper/770563/en

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