DocumentCode :
2528965
Title :
A performance comparison of using principal component analysis and ant clustering with fuzzy c-means and k-harmonic means
Author :
Julrode, P. ; Supratid, Siriporn ; Suksawatchon, Ureerat
Author_Institution :
Dept. of Inf. Technol., Rangsit Univ., Patumthani, Thailand
fYear :
2012
fDate :
12-14 July 2012
Firstpage :
123
Lastpage :
128
Abstract :
Several clustering researches focus on the idea for achieving the optimal initial set of clusters before performing further clustering. This may be accomplished by performing two-level clustering. However, such an idea may possibly not either significantly improve the accuracy rate or well alleviate local traps; contrarily it usually generates abundant runtime consumption. Thereby, one may turn to focus on the relieving the problems of high dimensional, noisy data and hidden outliers. Such difficulties usually occur in real-world environment; and can seriously spoil the computation of several of types of learning, including clustering. This paper proposes a performance comparison using feature reduction based method, principal component analysis (PCA) and ant clustering algorithm combining with two particular fuzzy clustering approaches, fuzzy c-means (FCM) and k-harmonic means (KHM). FCM and KHM are soft clustering algorithms that retain more information from the original data than those of crisp or hard. PCA is employed as preprocess of FCM and KHM for relieving the curse of high-dimensional, noisy data. Ant clustering algorithm is employed as the first level of clustering that supplies the optimal set of initial clusters to those soft clustering methods. Comparison tests among related methods, PCA-FCM, PCA-KHM, ANT-FCM and ANT-KHM are evaluated in terms of clustering objective function, adjusted rand index and time consumption. Seven well-known benchmark real-world data sets are employed in the experiments. Within the scope of this study, the superiority of using PCA for feature reduction over the two-level clustering, ANT-FCM and ANT-KHM is pointed out.
Keywords :
fuzzy set theory; pattern clustering; principal component analysis; ANT-FCM; ANT-KHM; PCA-FCM; PCA-KHM; Rand index; ant clustering; clustering objective function; feature reduction; fuzzy c-means; fuzzy clustering; k-harmonic means; principal component analysis; soft clustering algorithm; time consumption; two-level clustering; Algorithm design and analysis; Clustering algorithms; Eigenvalues and eigenfunctions; Linear programming; Noise measurement; Principal component analysis; Runtime; Principal component analysis; ant clustering; fuzzy c-means; k-harmonic means;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Cybernetics (CyberneticsCom), 2012 IEEE International Conference on
Conference_Location :
Bali
Print_ISBN :
978-1-4673-0891-5
Type :
conf
DOI :
10.1109/CyberneticsCom.2012.6381631
Filename :
6381631
Link To Document :
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