Volume 11, Issue 1 (1-2019)                   IJICTR 2019, 11(1): 36-44 | Back to browse issues page

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1- Electrical & Computer Engineering Dept. Kharazmi University, Tehran, Iran
2- Electrical & Computer Engineering Dept. Kharazmi University, Tehran, Iran , kelarestaghi@khu.ac.ir
Abstract:   (398 Views)
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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Type of Study: Applicable | Subject: Information Technology