Title :
Immune Algorithm for Supervised Clustering
Author :
Xu, Lifang ; Mo, Hongwei ; Wang, Kejun
Author_Institution :
Autom. Coll., Harbin Eng. Univ., Hongwei
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;
Conference_Titel :
Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0475-4
DOI :
10.1109/COGINF.2006.365622
