Title :
Clustering compatible objects by point neighborhood
Author :
Wan, Renxia ; Wang, Lixin ; Hao, Zijun
Author_Institution :
Coll. of Inf. & Comput. Sci., North Univ. for Nat., Yinchuan, China
Abstract :
In some cases, clustering objects into several compatible clusters is more rational than traditional clustering methods do. In this paper, we propose a new compatible clustering algorithm based on CompClustering, it adopts point neighborhood technique to replace the iterative mechanism of the latter. Experiments show that the proposed algorithm can get some consistent clustering results, and theory analysis also demonstrates that the proposed algorithm has lower computation consumption than CompClustering does.
Keywords :
data analysis; object-oriented methods; pattern clustering; statistical analysis; CompClustering; compatible object clustering; point neighborhood technique; Educational institutions; clustering; compatible relation; neighbourhood; object set;
Conference_Titel :
Artificial Intelligence and Education (ICAIE), 2010 International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-6935-2
DOI :
10.1109/ICAIE.2010.5641428