DocumentCode
2678823
Title
Immune Algorithm for Supervised Clustering
Author
Xu, Lifang ; Mo, Hongwei ; Wang, Kejun
Author_Institution
Autom. Coll., Harbin Eng. Univ., Hongwei
Volume
2
fYear
2006
fDate
17-19 July 2006
Firstpage
953
Lastpage
958
Abstract
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
Keywords
data mining; learning (artificial intelligence); pattern clustering; class-uniform cluster; data mining; immune supervised clustering; Automation; Classification algorithms; Clustering algorithms; Cognitive informatics; Data engineering; Data mining; Educational institutions; Iris; Runtime; Unsupervised learning; Clustering for classification; Immune algorithm; Supervised clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
1-4244-0475-4
Type
conf
DOI
10.1109/COGINF.2006.365622
Filename
4216540
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