1 research outputs found
Efficient Candidacy Reduction For Frequent Pattern Mining
Certainly, nowadays knowledge discovery or extracting knowledge from large
amount of data is a desirable task in competitive businesses. Data mining is a
main step in knowledge discovery process. Meanwhile frequent patterns play
central role in data mining tasks such as clustering, classification, and
association analysis. Identifying all frequent patterns is the most time
consuming process due to a massive number of candidate patterns. For the past
decade there have been an increasing number of efficient algorithms to mine the
frequent patterns. However reducing the number of candidate patterns and
comparisons for support counting are still two problems in this field which
have made the frequent pattern mining one of the active research themes in data
mining. A reasonable solution is identifying a small candidate pattern set from
which can generate all frequent patterns. In this paper, a method is proposed
based on a new candidate set called candidate head set or H which forms a small
set of candidate patterns. The experimental results verify the accuracy of the
proposed method and reduction of the number of candidate patterns and
comparisons.Comment: 8 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS December 2009, ISSN 1947 5500,
http://sites.google.com/site/ijcsis