TY - GEN
T1 - Incrementally mining recently repeating patterns over data streams
AU - Koh, Jia Ling
AU - Chou, Pei Min
N1 - Funding Information:
This work was partially supported by the R.O.C. N.S.C. under Contract No. 96-2221-E-003-018 and 96-2524-S-003-001.
PY - 2009
Y1 - 2009
N2 - Repeating patterns represent temporal relations among data items, which could be used for data summarization and data prediction. More and more data of various applications is generated as a data stream. Based on time sensitive concern, mining repeating patterns from the whole history data sequence of a data stream does not extract the current trend of patterns over the stream. Therefore, the traditional strategies for mining repeating patterns on static database are not applicable to data streams. For this reason, an algorithm, named appearing-bit-sequence-based incremental mining algorithm, for efficiently discovering recently repeating patterns over a data stream is proposed in this paper. The appearing bit sequences are used to monitor the occurrences of patterns within a sliding window. Two versions of algorithms are proposed by maintaining the appearing bit sequences of maximum repeating patterns and closed repeating patterns, respectively. Accordingly, the cost of re-mining repeating patterns over a sliding window is reduced to that of monitoring frequency changes of the maintained patterns. The experimental results show that the incremental mining methods perform much better than the re-miming approach.
AB - Repeating patterns represent temporal relations among data items, which could be used for data summarization and data prediction. More and more data of various applications is generated as a data stream. Based on time sensitive concern, mining repeating patterns from the whole history data sequence of a data stream does not extract the current trend of patterns over the stream. Therefore, the traditional strategies for mining repeating patterns on static database are not applicable to data streams. For this reason, an algorithm, named appearing-bit-sequence-based incremental mining algorithm, for efficiently discovering recently repeating patterns over a data stream is proposed in this paper. The appearing bit sequences are used to monitor the occurrences of patterns within a sliding window. Two versions of algorithms are proposed by maintaining the appearing bit sequences of maximum repeating patterns and closed repeating patterns, respectively. Accordingly, the cost of re-mining repeating patterns over a sliding window is reduced to that of monitoring frequency changes of the maintained patterns. The experimental results show that the incremental mining methods perform much better than the re-miming approach.
KW - Data streams
KW - Incremental mining
KW - Repeating patterns
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U2 - 10.1007/978-3-642-00399-8_3
DO - 10.1007/978-3-642-00399-8_3
M3 - Conference contribution
AN - SCOPUS:67650474316
SN - 3642003982
SN - 9783642003981
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 26
EP - 37
BT - New Frontiers in Applied Data Mining - PAKDD 2008 International Workshops, Revised Selected Papers
PB - Springer Verlag
T2 - Pacific Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
Y2 - 20 May 2008 through 23 May 2009
ER -