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National Chung Hsing University Institutional Repository - NCHUIR > 理學院 > 資訊科學與工程學系所 > 依資料類型分類 > 期刊論文 >  An adaptive approximation method to discover frequent itemsets over sliding-window-based data streams

Please use this identifier to cite or link to this item: http://nchuir.lib.nchu.edu.tw/handle/309270000/127010

標題: An adaptive approximation method to discover frequent itemsets over sliding-window-based data streams
作者: Li, Chao-Wei;Jea, Kuen-Fang
Contributors: 國立中興大學資訊科學與工程學系
National Chung Hsing University,Department of Computer Science and Engineering
Miao-zhen Luo
關鍵字: Data stream;Frequent itemset;Sliding window;Combinatorial;Approximation;Adaptive approximation;Concept drift
日期: 2011-9
Issue Date: 2012-10-26 14:46:54 (UTC+8)
摘要: Frequent-pattern discovery in data streams is more challenging than that in traditional databases since several requirements need to be additionally satisfied. For the sliding-window model of data streams, transactions both enter into and leave from the window at each sliding. In this paper, we propose an approximation method for mining frequent itemsets over the sliding window of a data stream. The proposed method could approximate itemsets' counts from the counts of their subsets instead of scanning the transactions for them. By noticing the more dynamic feature of sliding-window model, we have made an effort to devise a promising technique which enables the proposed method to approximate for itemsets adaptively. In addition, another technique which may adjust and correct the approximations is also designed. Empirical results have shown that the performance of proposed method is quite efficient and stable; moreover, the mining result from adaptive approximation (and approximation adjustment) achieves high accuracy. (C) 2011 Elsevier Ltd. All rights reserved.
Relation: Expert Systems with Applications, Volume 38, Issue 10, Page(s) 13386-13404.
Appears in Collections:[依資料類型分類] 期刊論文

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