Title :
A Novel Hybrid Clustering Algorithm Incorporating K-Means into Canonical Immune Programming Algorithm
Author :
Xing, Xiao-Shuai ; Li, Zhu ; Zhang, Qing-Quan ; Yang, Pei-Lin ; Yao, Jian-Bin
Author_Institution :
Coll. of Phys. & Inf. Eng., Shan Xi Normal Univ., Lin Fen, China
Abstract :
A novel Hybrid Clustering Algorithm (HCA) that incorporates the K-means into the canonical Immune Programming Algorithm (IPA) is proposed after analyzing the advantages and disadvantages of the classical k-means clustering algorithm in the paper. The theory analysis and experimental results show the algorithm not only avoids the local optima and is robust to initialization, but also increases the convergent speed, meanwhile evidently restrains the degenerating phenomenon during the evolutionary process.
Keywords :
pattern clustering; canonical immune programming algorithm; hybrid clustering algorithm; k-means clustering algorithm; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Convergence; Pattern recognition; Programming; Robustness;
Conference_Titel :
Multimedia Technology (ICMT), 2010 International Conference on
Conference_Location :
Ningbo
Print_ISBN :
978-1-4244-7871-2
DOI :
10.1109/ICMULT.2010.5629894