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

fYear

2009

fDate

4-7 Dec. 2009

Firstpage

19

Lastpage

24

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/SoCPaR.2009.17

Filename

5369315