Sliding-Window Filtering: An Efficient Algorithm for Incremental Mining

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 ...

• Type: Conference
• Year: 2001
• Where: CIKM
• Authors: Chang-Hung Lee, Cheng-Ru Lin, Ming-Syan Chen
• Keywords: Algorithm Swf, CIKM 2001, Incremental Mining, Information Management, Scan Reduction Techniques