Title :
A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data
Author :
Rasouli, Abdolreza ; Bin Maarof, M.A. ; Shamsi, Mahboubeh
Author_Institution :
Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai, Malaysia
Abstract :
Clustering is the process of gathering objects into groups based on their feature´s similarity. In this paper, we concentrate on Weighted Kernel K-Means method for its capability to manage nonlinear separability and high dimensionality in the data. A new slight modification of WKM algorithm has been proposed and tested on real Rice data. The results show that the accuracy of proposed algorithm is higher than other famous clustering algorithm and ensures that the WKM is a good solution for real world problems.
Keywords :
data mining; pattern clustering; Rice data; clustering method; data mining; nonlinear separability; weighted kernel k-means methods; Atmospheric modeling; Clustering algorithms; Clustering methods; Computer science; Data mining; Kernel; Machine learning algorithms; Management information systems; Pattern recognition; Vegetation mapping; Classification Accuracy; Clustering; Data Mining; F-Measure; WKM Algorithm; Weighted Kernel K-Means;
Conference_Titel :
Soft Computing and Pattern Recognition, 2009. SOCPAR '09. International Conference of
Conference_Location :
Malacca
Print_ISBN :
978-1-4244-5330-6
Electronic_ISBN :
978-0-7695-3879-2
DOI :
10.1109/SoCPaR.2009.17
