Title :
Interval ckMeans: An algorithm for clustering symbolic data
Author :
De Vargas, Rogério R. ; Bedregal, Benjamín R C
Author_Institution :
Dept. of Inf. & Appl. Math., Fed. Univ. of Rio Grande do Norte, Natal, Brazil
Abstract :
Clustering is the process of organizing a collection of patterns into groups based on their similarities. Fuzzy clustering techniques aim at finding groups to which every object in the database belongs to some membership degree. This paper presents a new algorithm for clustering symbolic data based on ckMeans algorithm. This new algorithm allows the data entry and the membership degree to be intervals. In order to validate the proposal, it is compared to two other algorithms using the same database.
Keywords :
data analysis; fuzzy set theory; pattern clustering; fuzzy clustering technique; interval ckMeans algorithm; membership degree; pattern collection organization; symbolic data clustering; Cities and towns; Clustering algorithms; Equations; Measurement; Partitioning algorithms; Support vector machines;
Conference_Titel :
Fuzzy Information Processing Society (NAFIPS), 2011 Annual Meeting of the North American
Conference_Location :
El Paso, TX
Print_ISBN :
978-1-61284-968-3
Electronic_ISBN :
Pending
DOI :
10.1109/NAFIPS.2011.5752042
