DocumentCode :
1935079
Title :
Rough k-means Cluster with Adaptive Parameters
Author :
Tao Zhou ; Yan-ning Zhang ; Hui-Ling Lu
Author_Institution :
Northwestern Polytech. Univ., Xi´an
Volume :
6
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
3063
Lastpage :
3068
Abstract :
In this paper, we firstly analyze Lingras algorithm with respect to its objective-function, numerical stability of the clusters. Then we point out its shortcoming in adjusting the three coefficients omegaiota , omegaupsi and epsiv . To tackle this problem, a rough k-means clustering method is finally presented with adaptive parameters. This algorithm is used in a testing sample and obtains a less error clustering rate.
Keywords :
numerical stability; pattern clustering; rough set theory; Lingras algorithm; numerical stability; objective-function; rough k-means clustering method; Algorithm design and analysis; Clustering algorithms; Clustering methods; Cybernetics; Data analysis; Machine learning; Numerical stability; Pattern classification; Rough sets; Testing; Adaptive parameters; Clustering algorithm; Rough k-means; Rough sets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370674
Filename :
4370674
Link To Document :
https://search.ricest.ac.ir/dl/search/defaultta.aspx?DTC=49&DC=1935079
بازگشت