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題名 Mining generalized fuzzy association rules from web pages
作者 Tang, Yi-Tsung
Chiu, Hung-Pin
關鍵詞 模糊資料挖掘;關聯法則
Fuzzy data mining;association rules
日期 2005
上傳時間 29-九月-2017 17:34:19 (UTC+8)
摘要 模糊關聯法則的挖掘是資料挖掘(Data Mining)中一個重要的部分,也有許多的方法相繼被提出。然而,這些演算法對於處理實際資料上的效率仍然有改進的空間。本研究提出了一個有效率的方法(Cluster-Based Fuzzy Association Rule:CBFAR)來從許多網頁中找出模糊關聯法則,並改進挖掘的處理效率,此方法以分群表(cluster table)的關念來儲存網頁瀏覽次數之模糊值,在大項目組的產生過程中,只需掃描瀏覽資料庫一次並去除許多不必要的資料比對時間,有效的減少處理時間,改進效率。
The discovery of fuzzy association rules is an important data-mining task for which many algorithms have been proposed. However, the efficiency of these algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient method named cluster-based fuzzy association rule (CBFAR) to discover generalized fuzzy association rules from web pages. The CBFAR method is to create fuzzy cluster tables by scanning the browse information database (BIDB) once, and then clustering the browse records to the k-th cluster table, where the length of a record is k. The counts of the fuzzy regions are stored in the Fuzzy_Cluster Tables. This method requires less contrast to generate large itemsets. The CBFAR method is also discussed.
關聯 TANET 2005 台灣網際網路研討會論文集
電子商務與電子化政府
資料類型 conference
dc.creator (作者) Tang, Yi-Tsungen_US
dc.creator (作者) Chiu, Hung-Pinen_US
dc.date (日期) 2005
dc.date.accessioned 29-九月-2017 17:34:19 (UTC+8)-
dc.date.available 29-九月-2017 17:34:19 (UTC+8)-
dc.date.issued (上傳時間) 29-九月-2017 17:34:19 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/113246-
dc.description.abstract (摘要) 模糊關聯法則的挖掘是資料挖掘(Data Mining)中一個重要的部分,也有許多的方法相繼被提出。然而,這些演算法對於處理實際資料上的效率仍然有改進的空間。本研究提出了一個有效率的方法(Cluster-Based Fuzzy Association Rule:CBFAR)來從許多網頁中找出模糊關聯法則,並改進挖掘的處理效率,此方法以分群表(cluster table)的關念來儲存網頁瀏覽次數之模糊值,在大項目組的產生過程中,只需掃描瀏覽資料庫一次並去除許多不必要的資料比對時間,有效的減少處理時間,改進效率。
dc.description.abstract (摘要) The discovery of fuzzy association rules is an important data-mining task for which many algorithms have been proposed. However, the efficiency of these algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient method named cluster-based fuzzy association rule (CBFAR) to discover generalized fuzzy association rules from web pages. The CBFAR method is to create fuzzy cluster tables by scanning the browse information database (BIDB) once, and then clustering the browse records to the k-th cluster table, where the length of a record is k. The counts of the fuzzy regions are stored in the Fuzzy_Cluster Tables. This method requires less contrast to generate large itemsets. The CBFAR method is also discussed.
dc.format.extent 159720 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) TANET 2005 台灣網際網路研討會論文集zh_TW
dc.relation (關聯) 電子商務與電子化政府zh_TW
dc.subject (關鍵詞) 模糊資料挖掘;關聯法則zh_TW
dc.subject (關鍵詞) Fuzzy data mining;association rulesen_US
dc.title (題名) Mining generalized fuzzy association rules from web pagesen_US
dc.type (資料類型) conferenceen