Title :
Weight Computing in Competitive K-Means Algorithm
Author :
Cui, Tingting ; Li, Fangshi
Author_Institution :
Coll. of Appl. Sci., Beijing Univ. of Technol., Beijing, China
Abstract :
This paper presents Weight Computing in Competitive K-Means Algorithm which is derived from Improved K-means method and subspace clustering. By adding weights to the objective function, the contributions from each feature of each clustering could simultaneously minimize the separations within clusters and maximize the separation between clusters. The experiments described in this paper confirm good performance of the proposed algorithm.
Keywords :
pattern clustering; competitive k-means algorithm; improved k-means method; objective function; separation between cluster maximization; separation within cluster minimization; subspace clustering; weight computing; Accuracy; Algorithm design and analysis; Clustering algorithms; Complexity theory; Entropy; Partitioning algorithms; Signal processing algorithms; Clustering algorithm; K-means algorithm;
Conference_Titel :
Computing, Communications and Applications Conference (ComComAp), 2012
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4577-1717-8
DOI :
10.1109/ComComAp.2012.6154887
