教師資料查詢 | 類別: 期刊論文 | 教師: 陳俊豪 CHUN-HAO CHEN (瀏覽個人網頁)

標題:A fuzzy coherent rule mining algorithm
學年101
學期2
出版(發表)日期2013/07/01
作品名稱A fuzzy coherent rule mining algorithm
作品名稱(其他語言)一個模糊一致性規則探勘演算法
著者Chen, Chun-Hao; Li, Ai-Fang; Lee, Yeong-Chyi
單位淡江大學資訊工程學系
出版者Amsterdam: Elsevier BV;Elsevier BV
著錄名稱、卷期、頁數Applied Soft Computing 13(7), pp.3422–3428
摘要In real-world applications, transactions usually consist of quantitative values. Many fuzzy data mining approaches have thus been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, the common problems of those approaches are that an appropriate minimum support is hard to set, and the derived rules usually expose common-sense knowledge which may not be interesting in business point of view. In this paper, an algorithm for mining fuzzy coherent rules is proposed for overcoming those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy the four criteria or not. If yes, it is a fuzzy coherent rule. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.;在真實的應用中,交易資料通常包含數值型資料。因此,在事前設定好的最小支持度下,許多模糊資料探勘方法被提出來從數值型資料中探勘模糊關聯規則。然而,這些方法的共通問題,第一、這些方法的最小支持度不易設定;第二、所探勘出的規則只揭露常識性的資訊,使得這些規則不具有商業價值。在本論文,我們提出一個具有命題邏輯性的模糊一致性規則探勘演算法克服上述問題。他首先將數值型資料轉換成模糊集合。之後,根據產生出來的模糊集進一步產生候選模糊一致性規則。最後,計算每條候選模糊一致性規則的列聯表,並利用它來檢查規則是否有滿足命題邏輯的四個標準。如果是,該規則就是一條模糊一致性規則。在實驗部份,透過foodmart資料集一顯示所提的演算法是有效的。
關鍵字fuzzy set; fuzzy association rules; fuzzy coherent rules; membership function; data mining; 模糊集; 模糊關聯規則; 模糊一致性規則; 隸屬函數; 資料探勘
語言英文(美國)
ISSN1568-4946;1872-9681
期刊性質國外
收錄於
產學合作
通訊作者Chen, Chun-Hao
審稿制度
國別荷蘭
公開徵稿
出版型式紙本;電子版
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