An Idea of Improvement Decision Tree Learning Using Cluster Analysis

被引：2

作者：

Amanuma, Saori ^{[1
]}

Kurematsu, Masaki ^{[1
]}

Fujita, Hamido ^{[1
]}

机构：

[1] Iwate Prefectual Univ, Grad Sch Software & Informat, Takizawa, Iwate, Japan

来源：

NEW TRENDS IN SOFTWARE METHODOLOGIES, TOOLS AND TECHNIQUES | 2012年 / 246卷

关键词：

Decision Tree; Cluster analysis; Machine Learning; Within-class variance; Between-class variance;

D O I：

10.3233/978-1-61499-125-0-351

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In this paper, we proposed an idea of improvement of a decision tree learning algorithm using cluster analysis. We classify data set based on two relations. One is the relation between each class and each attribute and the other is the relation between attributes. First relation is used in a traditional decision tree algorithm and second relation is used in cluster analysis. Using second relation is our point in this approach. In order to evaluate our approach, we did an experiment using data set in machine learning repositories. Experimental result show the possibility that our approach is better than a traditional decision tree learning algorithm.

引用

页码：351 / 358

页数：8

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