From Fuzzy Association Rule Mining to Effective Classification Framework

被引：0

作者：

Alhawsawi, Osama ^{[1
]}

AL-Saidi, Mayad ^{[1
]}

Phi, Michael ^{[1
]}

Jarada, Tamer N. ^{[1
]}

Khabbaz, Mohammad ^{[2
]}

Koockakzadeh, Negar ^{[1
]}

Kianmehr, Keivan ^{[3
]}

Alhajj, Reda ^{[1
,4
]}

Rokne, Jon ^{[1
]}

机构：

[1] Univ Calgary, Dept Comp Sci, Calgary, AB T2N 1N4, Canada

[2] Univ British Columbia, Dept Comp Sci, Vancouver, BC, Canada

[3] Univ Western Ontario, Dept Elect & Comp Engn, London, ON, Canada

[4] Global Univ, Dept Comp Sci, Beirut, Lebanon

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2011 | 2011年 / 6936卷

关键词：

Fuzzy Sets; Fuzzy Association Rules; Data Mining; Classification; APRIORI; Fuzzy Partitioning; Sharp Boundary;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Given a set of known classes, classification is a two steps process which uses part of the data to build a model capable of determining the class of new objects not used in the training phase. The accuracy of the classifier is one of the main criteria to judge its usefulness. However, most of the existing classification approaches decide on a single class for a given object. We argue that fuzzy classification is more attractive because it is closer to the real case where it is hard to identify a unique one class per object. To tackle this problem, we developed a framework which produces fuzzy association rules and uses them to build the classifier model. There are two important factors to consider: the method to create fuzzy association rules must be accurate, and the method to build a classifier must be accurate as well. In this paper, we will describe a method to perform fuzzy association rule mining and classification and we will test our results based on numerous factors including accuracy, varying levels of support and confidence.

引用

页码：413 / +

页数：2

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