Hemalatha, R; Krishnan, A; Hemamathi, R(INFLIBNET Centre, February 2, 2005)
[more]
[less]
Abstract:
Correlated The discovery of association rules is an important problem in data mining. It is
a two-step process consisting of finding the frequent itemsets and generating association
rules from them. Most of the research attention is focused on efficient methods of finding
frequent itemsets because it is computationally the most expensive step. This paper presents
a new data structure and a more efficient algorithm for mining frequent itemsets from typical
data sets. The improvement is achieved by scanning the database just once and by reducing
item traversals within transactions. The performance comparisons of the algorithm against
the fastest Apriori implementation and the recently developed H-Mine algorithm are given
here. These results show that the algorithm outperforms both Apriori and H-mine on several
widely used test data sets.