DocumentCode :
693136
Title :
Clustering ensemble method based DILCA distance
Author :
Bao-Ping Su ; Ming Chunwang ; Yuan-Yuan Sun ; Kun Liu
Author_Institution :
Sch. of Sci., Tianjin Univ. of Technol. & Educ., Tianjin, China
Volume :
01
fYear :
2013
fDate :
14-17 July 2013
Firstpage :
29
Lastpage :
34
Abstract :
A method of clustering ensemble is transforming the clustering ensemble problem into the clustering problem among objects in a nominal information table. The basic problem is to give a method which is used to calculate the distance between the nominal attribute value. In this paper, DILCA method is adopted to calculate the distance between the nominal attribute value. Using the correlation between the attributes, this method calculate the distance more accurately. At the same time, the method uses the correlation and redundancy between attributes to decide the context attributes set of one attribute which is used to reduce the calculation quantity. The superiority of this method are demonstrated by experiments.
Keywords :
pattern clustering; clustering ensemble method based DILCA distance; context attributes set; nominal attribute value; nominal information table; Abstracts; Chebyshev approximation; Computers; Correlation; Ionosphere; Iris; Robustness; Clustering ensemble; DILCA; Information Gain; context;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
Conference_Location :
Tianjin
Type :
conf
DOI :
10.1109/ICMLC.2013.6890439
Filename :
6890439
Link To Document :
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