A Study on the Performance Of Ct-Apriori And Ct-Pro Algorithms Using Compressed Structures For Pattern Mining

A B Dhivya

A Study on the Performance Of Ct-Apriori And Ct-Pro Algorithms Using Compressed Structures For Pattern Mining

dc.contributor.advisor	B Kalpana
dc.contributor.author	A B Dhivya
dc.date.accessioned	2016-12-28T23:54:58Z
dc.date.available	2016-12-28T23:54:58Z
dc.date.issued	2010-09
dc.department	Computer Science	en_US
dc.description.abstract	Automatic discovery and identification of frequently occurring patterns from very large database is the most desired technique in businesses and industries. Data mining and knowledge discovery provides several powerful algorithms for this purpose. Frequent pattern mining is a technique that is used to discover patterns from large transactional database. The frequent pattern algorithms perform the mining process in two phases. In the first phase, all frequent itemsets that satisfy the user specified minimum support are generated and in the second phase uses these frequent itemsets in order to discover all the association rules that meet a confidence threshold. This research analyzes algorithms that produce compact databases for knowledge discovery from large transaction databases like market basket database and web log databases. From these compact representations, association rule mining is applied to mine frequent patterns. In this research, two variants of Apriori and FP-Growth algorithms, namely, CT-Apriori and CT-PRO are compared and their performances are analyzed.	en_US
dc.identifier.uri	https://ir.avinuty.ac.in/handle/avu/448
dc.lang	English	en_US
dc.title	A Study on the Performance Of Ct-Apriori And Ct-Pro Algorithms Using Compressed Structures For Pattern Mining	en_US