Title :
A robust clustering algorithm for interval data
Author :
Yang, Miin-Shen ; Kuo, Hsien-Chun ; Hung, Wen-Liang
Author_Institution :
Dept. of Appl. Math., Chung Yung Christian Univ., Chungli, Taiwan
Abstract :
In this paper we propose a robust clustering algorithm for interval data. The proposed method is based on similarity measure that is not necessary to specify a cluster number and initials. Several numerical examples demonstrate the effectiveness of the proposed robust clustering algorithm. We then apply this algorithm to the real data set with cities temperature interval data. The proposed clustering algorithm actually presents its robustness.
Keywords :
pattern clustering; cities temperature interval data; cluster initials; cluster number; interval data; robust clustering algorithm; Algorithm design and analysis; Cities and towns; Clustering algorithms; Clustering methods; Correlation; Ocean temperature; Robustness; clustering algorithm; interval data; robustness;
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1507-4
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZ-IEEE.2012.6251364
