文章基本信息

标题：Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithm
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作者：Sanjeev Rao ; Priyanka Gupta
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：1Ver 3
出版社：Ayushmaan Technologies
摘要：In this paper we present new scheme for extracting association rules that considers the time, number of database scans, memory consumption, and the interestingness of the rules. Discover a FIS data mining association algorithm that removes the disadvantages of APRIORI algorithm and is efficient in terms of number of database scan and time. The frequent patterns algorithm without candidate generation eliminates the costly candidate generation. It also avoids scanning the database again and again. So, we use Frequent Pattern (FP) Growth ARM algorithm that is more efficient structure to mine patterns when database grows..
关键词：Data Mining; Association Rule Mining Algorithms; Apriori;Algorithm; FP-Growth Algorithm; Unsupervised Learning; Early;Pruning; etc.