Abstract
Incremental association mining refers to the maintenance and utilization of the knowledge discovered in the previous mining operations for later association mining. Sliding window filtering (SWF) is a technique proposed to filter false candidate 2-itemsets by segmenting a transaction database into partitions. In this paper, we extend SWF by incorporating previously discovered information and propose two algorithms to boost the performance for incremental mining. The first algorithm FI_SWF (SWF with Frequent Itemset) reuses the frequent itemsets of previous mining task to reduce the number of new candidate itemsets that have to be checked. The second algorithm CI_SWF (SWF with Candidate Itemset) reuses the candidate itemsets from the previous mining task. Experiments show that the new proposed algorithms are significantly faster than SWF.
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© 2003 Springer-Verlag Berlin Heidelberg
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Chang, CH., Yang, SH. (2003). Enhancing SWF for Incremental Association Mining by Itemset Maintenance. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_30
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DOI: https://doi.org/10.1007/3-540-36175-8_30
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