By Topic

Cluster-Based Evaluation in Fuzzy-Genetic Data Mining

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chun-Hao Chen ; Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng-Kung Univ., Tainan ; Tseng, V.S. ; Tzung-Pei Hong

Data mining is commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transactions in real-world applications, however, usually consist of quantitative values. In the past, we proposed a fuzzy-genetic data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. It used a combination of large 1-itemsets and membership-function suitability to evaluate the fitness values of chromosomes. The calculation for large 1-itemsets could take a lot of time, especially when the database to be scanned could not totally fed into main memory. In this paper, an enhanced approach, called the cluster-based fuzzy-genetic mining algorithm, is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes in a population into clusters by the - means clustering approach and evaluates each individual according to both cluster and their own information. Experimental results also show the effectiveness and efficiency of the proposed approach.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:16 ,  Issue: 1 )