TY - GEN

T1 - Farthest centroids divisive clustering

AU - Fang, Haw Ren

AU - Saad, Yousef

PY - 2008/12/1

Y1 - 2008/12/1

N2 - A method is presented to partition a given set of data entries embedded in Euclidean space by recursively bisecting clusters into smaller ones. The initial set is subdivided into two subsets whose centroids are farthest from each other, and the process is repeated recursively on each subset. An approximate algorithm is proposed to solve the original integer programming problem which is NP-hard. Experimental evidence shows that the clustering method often outperforms a standard spectral clustering method, albeit at a slightly higher computational cost. The paper also discusses improvements of the standard K-means algorithm. Specifically, the clustering quality resulting from the K-means technique can be significantly enhanced by using the proposed algorithm for its initialization.

AB - A method is presented to partition a given set of data entries embedded in Euclidean space by recursively bisecting clusters into smaller ones. The initial set is subdivided into two subsets whose centroids are farthest from each other, and the process is repeated recursively on each subset. An approximate algorithm is proposed to solve the original integer programming problem which is NP-hard. Experimental evidence shows that the clustering method often outperforms a standard spectral clustering method, albeit at a slightly higher computational cost. The paper also discusses improvements of the standard K-means algorithm. Specifically, the clustering quality resulting from the K-means technique can be significantly enhanced by using the proposed algorithm for its initialization.

UR - http://www.scopus.com/inward/record.url?scp=60649118355&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=60649118355&partnerID=8YFLogxK

U2 - 10.1109/ICMLA.2008.141

DO - 10.1109/ICMLA.2008.141

M3 - Conference contribution

AN - SCOPUS:60649118355

SN - 9780769534954

T3 - Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

SP - 232

EP - 238

BT - Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

T2 - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Y2 - 11 December 2008 through 13 December 2008

ER -