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標題: An Effective Clustering Algorithm for Mining Frequent Itemsets
作者: 廖宜恩
關鍵字: 資訊科學--軟體
Data Mining
Frequent Itemset
Support-Propagation Method
摘要: Mining frequent itemsets is a key problem in many important mining applications, such asthe discovery of association rules, sequential patterns, and the network intrusion analysis.However, the task of discovering frequent itemsets in a large database is quite challenging.Traditional algorithms solve this problem by using a bottom-up breadth-first approach; theyuse a candidate generation method such that the frequent itemsets at a level can be used toconstruct candidate patterns at the next level. Nevertheless, it has the drawback of scanningthe database multiple times for verifying candidate itemsets. If there are lots of candidateitemsets, the cost of scanning the database may become very expensive. Thus, the goal of ourresearch is to develop a more effective algorithm for mining frequent itemsets.In this research project, we propose a new algorithm that will divide database items intoclusters and discover the frequent itemsets in each cluster by using the support-propagationmethod. Compared to the traditional algorithms, examining database items in a top-downmanner is the unique feature of the support-propagation method. As a result, it scans thedatabase only once. However, it may generate too many itemsets while decomposing thetransactions from the database. Therefore, we propose a clustering technique to improve thespace problem in the support-propagation method. Furthermore, we will design a simulationplatform to compare our top-down approach against the traditional bottom-up algorithms.
其他識別: NSC97-2221-E005-083
Appears in Collections:資訊科學與工程學系所



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