Presentation 2013-11-29
In this paper, we study on-line algorithms for mining a various kinds of frequent data on data stream.
Koji IWANUMA, Yoshitaka YAMAMOTO, Shuji ITO,
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Abstract(in English) Firstly, we give a brief survey the state-of-art online data mining algorithms for stream data. Secondly, we show a novel efficient on-line algorithm for extracting frequent subsequences from a multiple-data stream. This algorithm solves the important problem that a large amount of memories are suddenly consumed when bursty arrivals occurs in a data stream. The proposed on-line algorithm can limit the available memory to a given fixed space. The proposed algorithm have no false negatives under some conditions, and also have some other properties such as robustness.
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Keyword(in English) stream data / data mining / frequent data / on-line algorithm / sequential mining
Paper # AI2013-33
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Committee AI
Conference Date 2013/11/21(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) In this paper, we study on-line algorithms for mining a various kinds of frequent data on data stream.
Sub Title (in English)
Keyword(1) stream data
Keyword(2) data mining
Keyword(3) frequent data
Keyword(4) on-line algorithm
Keyword(5) sequential mining
1st Author's Name Koji IWANUMA
1st Author's Affiliation University of Yamanashi()
2nd Author's Name Yoshitaka YAMAMOTO
2nd Author's Affiliation University of Yamanashi
3rd Author's Name Shuji ITO
3rd Author's Affiliation University of Yamanashi
Date 2013-11-29
Paper # AI2013-33
Volume (vol) vol.113
Number (no) 332
Page pp.pp.-
#Pages 6
Date of Issue