Building a more accurate classifier based on strong frequent patterns

Yudho Giri Sucahyo, Raj P. Gopalan

Research output: Contribution to journalConference articlepeer-review

18 Citations (Scopus)


The classification problem in data mining is to discover models from training data for classifying unknown instances. Associative classification builds the classifier rules using association rules and it is more accurate compared to previous methods. In this paper, a new method named CSFP that builds a classifier from strong frequent patterns without the need to generate association rules is presented. We address the rare item problem by using a partitioning method. Rules generated are stored using a compact data structure named CP-Tree and a series of pruning methods are employed to discard weak frequent patterns. Experimental results show that our classifier is more accurate than previous associative classification methods as well as other state-of-the-art non-associative classifiers.

Original languageEnglish
Pages (from-to)1036-1042
Number of pages7
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Publication statusPublished - 2004
Event17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence - Cairns, Australia
Duration: 4 Dec 20046 Dec 2004


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