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标题：Generating associations rule mining using Apriori and FPGrowth Algorithms
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作者：J.Suresh ; P.Rushyanth ; Ch.Trinath 等
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2013
卷号：4
期号：4-5
出版社：Seventh Sense Research Group
摘要：Mining frequent itemsets from the large transactional database is one of the most challenging problems in data mining. In many real world scenarios, the data is not extracted from single data source but from distributed and heterogeneous ones. The discovered knowledge is expected to better business operations. In data mining methods association rule mining is one of the most popular one. However, mining informative patterns using association rules often results in a very large no of founding patterns, leaving the analyst with the task to go through all the rules and discover interesting ones In this paper we present a generating associations rules using Apriori and FPGrowth algorithms.
关键词：heterogeneous data; discovered knowledge; complex data; associations; frequent items; informative patterns