A two-phase fuzzy mining approach

Chun Wei Lin, Tzung Pei Hong, Wen Hsiang Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a two-phase fuzzy mining approach based on a tree structure to discover fuzzy frequent itemsets from a quantitative database. A simple tree structure called the upper-bound fuzzy frequent-pattern tree (abbreviated as UBFFP tree) is designed to help achieve the purpose. The two-phase fuzzy mining approach can easily derive the upper-bound fuzzy supports of itemsets through the tree and prune unpromising itemsets in the first phase, and then finds the actual frequent fuzzy itemsets in the second phase. Experimental results also show the good performance of the proposed approach.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010
DOIs
Publication statusPublished - 2010 Nov 25
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - Barcelona, Spain
Duration: 2010 Jul 182010 Jul 23

Publication series

Name2010 IEEE World Congress on Computational Intelligence, WCCI 2010

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
CountrySpain
CityBarcelona
Period10-07-1810-07-23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics

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