DocumentCode
456524
Title
Noise Clustering using a New Distance
Author
Wu, Xiao-Hong ; Zhou, Jian-Jiang
Author_Institution
Coll. of Inf. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut.
Volume
1
fYear
0
fDate
0-0 0
Firstpage
1938
Lastpage
1943
Abstract
Based on a new distance, a novel noise-resistant fuzzy clustering algorithm called alternative noise clustering (ANC) algorithm is proposed. ANC is an extension of the noise clustering (NC) algorithm proposed by Dave (1993). By replacing the Euclidean distance used in the objective function of NC algorithm, a new distance (non-Euclidean distance) is introduced in NC algorithm. Based on robust statistical point of view and influence function, the non-Euclidean distance is more robust than the Euclidean distance. So the ANC algorithm is more robust than the NC algorithm. Moreover, with the new distance ANC can deal with noises or outliers better than NC and fuzzy c-means (FCM). The better performance of the proposed algorithm is shown by performing experiments on data sets
Keywords
fuzzy set theory; noise; pattern clustering; alternative noise clustering; noise-resistant fuzzy clustering; nonEuclidean distance; Clustering algorithms; Computer vision; Digital images; Educational institutions; Equations; Euclidean distance; Noise robustness; Partitioning algorithms; Pattern recognition; Space technology;
fLanguage
English
Publisher
ieee
Conference_Titel
Information and Communication Technologies, 2006. ICTTA '06. 2nd
Conference_Location
Damascus
Print_ISBN
0-7803-9521-2
Type
conf
DOI
10.1109/ICTTA.2006.1684686
Filename
1684686
Link To Document