An ellipsoidal K-means for document clustering

被引：3
|
作者：
Dzogang, Fabon [1 ]
Marsala, Christophe [1 ]
Lesot, Marie-Jeanne [1 ]
Rifqi, Maria [2 ,3 ]
机构：
[1] Univ Paris 06, UMR7606, LIP6, Paris, France
[2] LIP6, Paris, France
[3] Univ Pantheon Assas, Paris, France
来源：
12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2012) | 2012年
关键词：
clustering; feature selection; spherical k-means; information retrieval;
D O I：
10.1109/ICDM.2012.126
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We propose an extension of the spherical K-means algorithm to deal with settings where the number of data points is largely inferior to the number of dimensions. We assume the data to lie in local and dense regions of the original space and we propose to embed each cluster into its specific ellipsoid. A new objective function is introduced, analytical solutions are derived for both the centroids and the associated ellipsoids. Furthermore, a study on the complexity of this algorithm highlights that it is of same order as the regular K-means algorithm. Results on both synthetic and real data show the efficiency of the proposed method.
引用
收藏
页码：221 / 230
页数：10
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