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Mining Top-K Fault Tolerant Frequent Patterns with Sliding Windows in Data Streams

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3 Author(s)
You Yuyang ; Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China ; Zhang JianPei ; Yang zhihong

Mining frequent patterns over streaming data has become an important research focus field with broad applications. However, the real-world data may be usually polluted by uncontrolled factors. Fault-tolerant frequent pattern can express more generalized information than frequent pattern which is absolutely matched. Therefore, a novel single-pass algorithm is proposed for efficiently mining top-k fault-tolerant frequent pattern from data streams without minimum support threshold specified by user. A novel data structure is developed for maintaining the essential information of itemsets generated so far. Experimental results show that the developed algorithm is an efficient method for mining top-k fault-tolerant frequent pattern from data streams.

Published in:

Intelligent Computing and Cognitive Informatics (ICICCI), 2010 International Conference on

Date of Conference:

22-23 June 2010

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