IMMUNE ALGORITHM FOR SUPERVISED CLUSTERING

被引：0

作者：

Xu, Lifang ^{[1
]}

Mo, Hongwei ^{[1
]}

Wang, Kejun ^{[1
]}

机构：

[1] Harbin Engn Univ, Automat Coll, Harbin, Peoples R China

来源：

PROCEEDINGS OF THE FIFTH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, VOLS 1 AND 2 | 2006年

关键词：

supervised clustering; immune algorithm; clustering for classification;

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：

This paper centers on a novel data mining technique we term immune supervised clustering. Unlike traditional clustering, immune supervised clustering assumes that the examples are classified by immune algorithm. The goal of immune supervised clustering algorithm(ISCA) is to identify class-uniform clusters that have high probability densities. The experimental results suggest that ISCA, although runtime intensive, finds the best clusters in almost all experiments conducted.

引用

页码：953 / 958

页数：6