Title :
Noise Clustering Using a New Distance
Author_Institution :
Coll. of Electr. & Inf. Eng., Jiangsu Univ., Zhenjiang
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. 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; pattern clustering; statistical analysis; alternative noise clustering algorithm; fuzzy c-means method; noise-resistant fuzzy clustering algorithm; nonEuclidean distance; statistical analysis; Clustering algorithms; Computer vision; Digital images; Educational institutions; Euclidean distance; Fuzzy sets; Noise robustness; Partitioning algorithms; Pattern recognition; Resists; Alternative noise clustering; Fuzzy clustering; Noise clustering;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Weihai
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305877