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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chun-Hao Chen Tzung-Pei Hong Tseng, V.S. Chang-Shing Lee |
| Copyright Year | 2007 |
| Description | Author affiliation: Nat. Cheng-Kung Univ., Tainan (Chun-Hao Chen) |
| Abstract | In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions under a single minimum support. In real applications, different items may have different criteria to judge their importance. In this paper, we thus propose an algorithm which combines clustering, fuzzy and genetic concepts for extracting reasonable multiple minimum support values, membership functions and fuzzy association rules form quantitative transactions. It first uses the k-means clustering approach to gather similar items into groups. All items in the same cluster are considered to have similar characteristics and are assigned similar values for initializing a better population. Each chromosome is then evaluated by the criteria of requirement satisfaction and suitability of membership functions to estimate its fitness value. Experimental results also show the effectiveness and the efficiency of the proposed approach. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 1705722 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424412099 |
| ISSN | 10987584 |
| DOI | 10.1109/FUZZY.2007.4295628 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-07-23 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data mining Association rules Fuzzy sets Itemsets Computer science Clustering algorithms Genetics Biological cells Algorithm design and analysis Application software |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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