An Ellipsoidal K-Means for Document Clustering

Fabon Dzogang (1) , Christophe Marsala (1) , Marie-Jeanne Lesot (1) , Maria Rifqi (1)

1 MALIRE - Machine Learning and Information Retrieval

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.

Mots clés: clustering; feature selection; spherical k-means; information retrieval

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]

Fabon Dzogang, Christophe Marsala, Marie-Jeanne Lesot, Maria Rifqi. An Ellipsoidal K-Means for Document Clustering. IEEE 12th International Conference on Data Mining (ICDM 2012), Dec 2012, Bruxelles, Belgium. pp.221-230, ⟨10.1109/ICDM.2012.126⟩. ⟨hal-01198898⟩