An efficient one-pass method for discovering bases of recently frequent episodes over online data streams

Gan, Min and Dai, Honghua 2012, An efficient one-pass method for discovering bases of recently frequent episodes over online data streams, International journal of innovative computing, information and control, vol. 8, no. 7A, pp. 4675-4690.

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Title An efficient one-pass method for discovering bases of recently frequent episodes over online data streams
Author(s) Gan, Min
Dai, Honghua
Journal name International journal of innovative computing, information and control
Volume number 8
Issue number 7A
Start page 4675
End page 4690
Total pages 16
Publisher ICIC International
Place of publication Kumamoto, Japan
Publication date 2012-07
ISSN 1349-4198
Keyword(s) Data streams
Online mining
Recently frequent episodes
Summary The 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.
Language eng
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, ICIC International
Persistent URL http://hdl.handle.net/10536/DRO/DU:30047064

Document type: Journal Article
Collection: School of Information Technology
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