Extending SWF for Incremental Association Mining by Incorporating Previously Discovered Information

• Main Authors: Shi-Hsan Yang, 楊士賢
• Other Authors: Chia-Hui Chang
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/69482842699773973055

碩士 === 國立中央大學 === 資訊工程研究所 === 90 === Incremental mining of association rules from dynamic databases refers to the maintenance and utilization of the knowledge discovered in the previous mining operations.Sliding- window-filtering (SWF)is a technique proposed to filtering false candidate 2-itemsets by segmenting a transaction database into several partitions.SWF computes a set of candidate 2-itemsets that is close to frequent 2-itemsets.Therefore,it is possible to generate several candidate k -itemsets for one database scan.Such a database scan reduction technique greatly increase the performance for frequent itemsets discovery.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)reuse the frequent itemsets (and the counts)of previous mining task as FUP2 to reduce the number of new candidate itemsets that have to be checked.The second algorithm CI SWF (SWF with Candidate Itemset)reuse the candidate itemsets (and the counts)from the previously mining task.Experimental studies are performed to evaluate performance of the new algorithms.The study shows that the new incremental algorithm is signi ficantly faster than SWF.More importantly,the need for more disk space to store the previously discovered knowledge does not increase the maximum memory required during the execution time.