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Thesis Abstract
There are various strategies for Data Mining such as Summarization, Classification, Clustering, Discovery of Associations rules and Analysis of the path changing.
At this research, after defining various data mining strategies, the association rules and their different kinds of them have been described. Then, different algorithms for discovering the association rules in database have been explained and the advantages and disadvantages of each algorithm have been noticed.
Most existing studies on association rules discovery focused on finding the association rules between all items in a large database that satisfy user-specified minimum confidence and support.
In practice, users are often interested in finding association rules involving only some specified items. Meanwhile, based on the search results, users tend to change the minimal confidence and support requirements to obtain suitable number of rules. Under these constraints, the existing mining algorithms can not perform efficiently due to high and repeated disk access overhead. In this research, we present a novel mining algorithm named creating sub vector (CS) ,which can efficiently discover the association rules between the user-specified items via feature extraction approach.
To present the efficiency of the proposed algorithm , we have compared it with two of bold and important algorithms called : Vector and CBAR algorithms and the results shows that the proposed algorithm has a better performance.
Keywords: Data mining, Itemset |
