Title :
IK-BKM: An incremental clustering approach based on intra-cluster distance
Author :
Ben Hariz, Sarra ; Elouedi, Zied
Author_Institution :
LARODEC, Inst. Super. de Gestion de Tunis, Le Bardo, Tunisia
Abstract :
This paper introduces a novel incremental approach to clustering uncertain categorical data. This so-called Incremental K Belief K-modes Method (IK-BKM) extends the Belief K-modes one to update the cluster partition when new information is available namely the increase of final desired clusters´ number. The main objective is to update clusters´ partition without complete reclustring. Our method will be illustrated by an example showing the comparative results of the incremental process and the non incremental one.
Keywords :
belief maintenance; data analysis; pattern clustering; cluster partition update; incremental clustering approach; incremental k belief k-modes method; intracluster distance; Clustering algorithms; Clustering methods; Context; Data mining; Heuristic algorithms; Training; Uncertainty; Incremental clustering; K-modes method; belief function theory; clusters´ number; intra-cluster dissimilarity measure;
Conference_Titel :
Computer Systems and Applications (AICCSA), 2010 IEEE/ACS International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4244-7716-6
DOI :
10.1109/AICCSA.2010.5587008