Title: Association Rules for Quantitative Data Mining
Abstract: Detatiled elaborations are presented for the idea on two-step frequent itemsets Apriori Algorrithm of Association Rules. over the years, a variety of algorithms for finding frequent item sets in very large transaction databases have been developed. The problems of finding frequent item sets are basic in association rule mining, fast algorithms for solving problems are needed. This paper presents an efficient version of apriori algorithm for mining association rules in large databases to finding maximum frequent itemset at lower level of abstraction. We propose a new, fast and an efficient algorithm with single scan of database for mining complete frequent item sets. To reduce the execution time and increase throughput in new method. Our proposed algorithm works well comparison with general approach of improved association rules. Apriori is the best-known algorithm to mine association rules. It uses a breadth-first search strategy to counting the support of itemsets and uses a candidate generation function which exploits the downward closure property of support. An improved method is called Improved Apriori Algorithm is brought forward owing to the disadvantages of Apriori Algorithm. Moreover,based on Improved Apriori Algorithm,data mining for market-basket analysis is carried out for the relationship between customers' transactions recurrences and products& attributes by making use of SQL Server 2005Analysis Services.
Publication Year: 2012
Publication Date: 2012-01-01
Language: en
Type: article
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Cited By Count: 1
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