期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：4
页码：1448-1452
出版社：TechScience Publications
摘要：Apriori is a classic algorithm for learning association rules. It is designed to operate on databases containing transactions .In This The candidate itemsets and a database is loaded into the hardware. But capacity of the hardware architecture is fixed. As number of candidate itemsets or the number of items in the database is larger than the hardware capacity. So That the items are loaded into the hardware separately, Due To this The time complexity is more to load candidate itemsets or database items into the hardware is in proportion to the number of candidate itemsets multiplied by the number of items .in the database. Increase Of candidate itemsets and a large database would create a performance blockage. we propose a Hash-based and Pipelined (HAPPI) architecture for hardware-enhanced association rule mining. By Using the pipeline methodology to compare itemsets with the database and gather useful information for reducing the number of candidate itemsets and items in the database concurrently. To find frequent itemsets the database is fed into the hardware, candidate itemsets are compared with the items in the database. At the same time, trimming information is collected from each transaction. Next itemsets are generated from transactions and hashed into a hash table. The useful trimming information and the hash table enable us to reduce the number of items in the database and the number of candidate itemsets. So that we can effectively reduce the frequency of loading the database into the hardware. Hashing And Pipelining solves the Performance bottleneck problem in a priori-based hardware schemes.
