Please use this identifier to cite or link to this item:
標題: Discovering frequent itemsets over transactional data streams through an efficient and stable approximate approach
作者: Jea, K.F.
Li, C.W.
關鍵字: Data mining;Data stream;Frequent itemset;Approximation;Combinatorics
Project: Expert Systems with Applications
期刊/報告no:: Expert Systems with Applications, Volume 36, Issue 10, Page(s) 12323-12331.
A data stream is a massive and unbounded sequence of data elements that are continuously generated at a fast speed. Compared with traditional approaches, data mining in data streams is more challenging since several extra requirements need to be satisfied. In this paper, we propose a mining algorithm for finding frequent itemsets over the transactional data stream. Unlike most of existing algorithms, our method works based on the theory of Approximate Inclusion-Exclusion. Without incrementally maintaining the overall synopsis of the stream, we can approximate the itemsets' counts according to certain kept information and the counts bounding technique. Some additional techniques are designed and integrated into the algorithm for performance improvement. Besides, the performance of the proposed algorithm is tested and analyzed through a series of experiments. (C) 2009 Elsevier Ltd. All rights reserved.
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2009.04.053
Appears in Collections:資訊科學與工程學系所

Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.