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  Sliding-window filtering: an efficient algorithm for incremental mining (2001) [19 citations — 6 self]

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by Chang-hung Lee, Cheng-ru Lin, Ming-syan Chen
In Proc. of the int’l conf. on Info. and knowledge management
http://www2.ee.ntu.edu.tw/~mschen/paperps/cikm414.pdf
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Abstract:

We explore in this paper an effective sliding-window filtering (abbreviatedly as SWF) algorithm for incremental mining of association rules. In essence, by partitioning a transaction database into several partitions, algorithm SWF employs a filtering threshold in each partition to deal with the candidate itemset generation. Under SWF, the cumulative information of mining previous partitions is selectively carried over toward the generation of candidate itemsets for the subsequent partitions. Algorithm SWF not only significantly reduces I/O and CPU cost by the concepts of cumulative filtering and scan reduction techniques but also effectively controls memory utilization by the technique of sliding-window partition. Algorithm SWF is particularly powerful for efficient incremental mining for an ongoing time-variant transaction database. By utilizing proper scan reduction techniques, only one scan of the incremented dataset is needed by algorithm SWF. The I/O cost of SWF is, in orders of magnitude, smaller than those required by prior methods, thus resolving the performance bottleneck. Experimental studies are performed to evaluate performance of algorithm SWF. It is noted that the improvement achieved by algorithm SWF is even more prominent as the incremented portion of the dataset increases and also as the size of the database increases.

Citations

364 Fast algorithms for mining association rules in large databases – Agrawal, Srikant - 1994
361 Mining Generalized Association Rules – Srikant, Agrawal - 1995
343 Dynamic itemset counting and implication rules for market basket data – Brin, Motwani, et al. - 1997
292 An e cient algorithm for mining association rules in large databases – Savasere, Omiecinski, et al. - 1995
280 Sampling Large Databases for Association Rules – Toivonen - 1996
149 Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique – Cheung, S, et al. - 1996
103 Efficient data mining for path traversal patterns – CHEN, PARK, et al. - 1998
102 A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing (Special Issue on High Performance Data Mining), (Accepted for Publication – Agarwal, Aggarwal, et al. - 1993
67 Optimization of constrained frequent set queries: 2-var constraints – Lakshmanan, Ng, et al. - 1998
66 Mining frequent item sets with convertible constraints – Pei, Han, et al. - 2001
60 Freespan: Frequent pattern-projected sequential pattern mining – Han, Pei, et al. - 2000
58 An efficient algorithm for the incremental updation of association rules – Thomas, Bodagala, et al. - 1997
47 Can we push more constraints into frequent pattern mining? SIGKDD’00 – Pei, Han - 2000
36 Constraintbased clustering in large databases – Tung, Ng, et al. - 2001
33 Mining association rules: Anti-skew algorithms – Lin, Dunham - 1997
9 An efficient algorithm to update large itemsets with early pruning – Ayn, Tansel, et al. - 1999
7 A general incremental technique for updating discovered association rules – Cheung, Lee, et al. - 1997
1 Algorithmsforassociationrulemining—ageneral survey and comparison – Hipp, Nakhaeizadeh - 2000
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