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An efficient one-pass method for discovering bases of recently frequent episodes over online data streams
journal contribution
posted on 2012-07-01, 00:00 authored by Min Gan, Honghua DaiThe knowledge embedded in an online data stream is likely to change over time due to the dynamic evolution of the stream. Consequently, infrequent episode mining over an online stream, frequent episodes should be adaptively extracted from recently generated stream segments instead of the whole stream. However, almost all existing frequent episode mining approaches find episodes frequently occurring over the whole sequence. This paper proposes and investigates a new problem: online mining of recently frequent episodes over data streams. In order to meet strict requirements of stream mining such as one-scan, adaptive result update and instant result return, we choose a novel frequency metric and define a highly condensed set called the base of recently frequent episodes. We then introduce a one-pass method for mining bases of recently frequent episodes. Experimental results show that the proposed method is capable of finding bases of recently frequent episodes quickly and adaptively. The proposed method outperforms the previous approaches with the advantages of one-pass, instant result update and return, more condensed resulting sets and less space usage.
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Journal
International journal of innovative computing, information and controlVolume
8Issue
7APagination
4675 - 4690Publisher
ICIC InternationalLocation
Kumamoto, JapanISSN
1349-4198Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2012, ICIC InternationalUsage metrics
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