Title :
MK-means - Modified K-means clustering algorithm
Author :
Dashti, Hesam T. ; Simas, Tiago ; Ribeiro, Rita A. ; Assadi, Amir ; Moitinho, Andre
Author_Institution :
Univ. of Wisconsin, Madison, WI, USA
Abstract :
This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected.
Keywords :
astronomy computing; pattern clustering; astrophysics data bases; density based clustering approach; guided kernel based clustering algorithm; modified K-means clustering algorithm; Color; Indexes; Variable speed drives;
Conference_Titel :
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6916-1
DOI :
10.1109/IJCNN.2010.5596300