期刊論文
| 學年 | 100 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2012-04-01 |
| 作品名稱 | Finding Pareto-front Membership Functions in Fuzzy Data Mining |
| 作品名稱（其他語言） | |
| 著者 | Chen, Chun-Hao; Hong, Tzung-Pei; Tseng, Vincent S. |
| 單位 | 淡江大學資訊工程學系 |
| 出版者 | Paris: Atlantis Press |
| 著錄名稱、卷期、頁數 | International Journal of Computational Intelligence Systems 5(2), pp.343-354 |
| 摘要 | Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very important. In the past, we proposed a fuzzy mining approach based on the Multi-Objective Genetic Algorithm (MOGA) to find the Pareto front of the desired membership functions. In this paper, we adopt a more sophisticated multi-objective approach, the SPEA2, to find the appropriate sets of membership functions for fuzzy data mining. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions and the second one is the total number of large 1-itemsets derived. Experimental comparisons of the proposed and the previous approaches are also made to show the effectiveness of the proposed approach in finding the Pareto-front membership functions. |
| 關鍵字 | multi-objective optimization; genetic algorithm; fuzzy set; fuzzy association rules; data mining; Pareto front |
| 語言 | en_US |
| ISSN | 1875-6891; 1875-6883 |
| 期刊性質 | 國外 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | Hong, Tzung-Pei |
| 審稿制度 | 是 |
| 國別 | FRA |
| 公開徵稿 | |
| 出版型式 | 紙本 電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92506 ) |