Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A localized algorithm for parallel association mining
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Proceedings of the tenth international conference on Information and knowledge management
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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Association rule mining is a data mining technique used to find interesting associations between items in a transaction database. Well known algorithms for association rule mining include Apriori and FP-tree. Apriori is a level wise algorithm and works by scanning the database multiple times. In an attempt to optimize the performance of Apriori, many variations of basic Apriori algorithm have been proposed. These variations exploit different approaches including reducing the number of database scans performed, using special data structures, using bitmaps and granular computing. Majority of these approaches are improvements in implementation of the same basic algorithm. In this paper we propose the RSO (Reduced Set Operations) algorithm, based on reducing the number of set operations performed. RSO is an algorithmic improvement to the basic Apriori algorithm; it is not an implementation improvement. Our analysis shows that RSO is asymptotically faster than Apriori. Experimental results also validate the efficiency of our algorithm.