Mining generalized fuzzy association rules from web pages

Abstract

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.