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    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/14998
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/14998

    Title: Effective Database Transformation and Efficient Support Computation for Mining Sequential Patterns
    Authors: C-W- Cho;Y-H- Wu;Chen, Arbee L. P.
    Keywords: Data mining;Sequential patterns;Database transformation;Support computation;Database projection
    Date: 2009-02
    Issue Date: 2008-12-16 16:43:39 (UTC+8)
    Abstract: In this paper, we propose a novel algorithm for mining frequent sequences from transaction databases. The transactions of the same customers form a set of customer sequences. A sequence (an ordered list of itemsets) is frequent if the number of customer sequences containing it satisfies the user-specified threshold. The 1-sequence is a special type of sequences because it consists of only a single itemset instead of an ordered list, while the k-sequence is a sequence composed of k itemsets. Compared with the cost of mining frequent k-sequences (k ≥ 2), the cost of mining frequent 1-sequences is negligible. We adopt a two-phase architecture to find the two types of frequent sequences separately in order that the discovery of frequent k-sequences can be well designed and optimized. For efficient frequent k-sequence mining, every frequent 1-sequence is encoded as a unique symbol and the database is transformed into one constituted by the symbols. We find that it is unnecessary to encode all the frequent 1-seqences, and make full use of the discovered frequent 1-sequences to transform the database into one with a smaller size. For every k ≥ 2, the customer sequences in the transformed database are scanned to find all the frequent k-sequences. We devise the compact representation for a customer sequence and elaborate the method to enumerate all distinct subsequences from a customer sequence without redundant scans. The soundness of the proposed approach is verified and a number of experiments are performed. The results show that our approach outperforms the previous works in both scalability and execution time.
    Relation: Journal of Intelligent Information Systems, 32(1), 23-51
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1007/s10844-007-0047-y
    DOI: 10.1007/s10844-007-0047-y
    Appears in Collections:[資訊科學系] 期刊論文

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