An intelligent Weighted Kernel K-Means algorithm for high dimension data

Author

Kenari, Abdolreza Rasouli ; Maarof, Mohd Aizaini Bin ; Sap, Mohd Noor Bin Md ; Shamsi, Mahboubeh

Author_Institution

Univ. Teknol. Malaysia, Malaysia

fYear

2009

fDate

4-6 Aug. 2009

Firstpage

829

Lastpage

831

Abstract

Clustering is a kind of unsupervised classification of objects into groups so that objects from the same cluster are more similar to each other than objects from different clusters. In this paper, we focus on Weighted Kernel K-Means method for its capability to handle nonlinear separability, noise, outliers and high dimensionality in the data. A new WKM algorithm has been proposed and tested on real Rice data. the results exposed by algorithm encourage the use of WKM for the solution of real world problems.

Keywords

data mining; pattern classification; pattern clustering; unsupervised learning; Rice data; high dimension data; intelligent weighted kernel K-means algorithm; noise; nonlinear separability; object clustering; outliers; unsupervised classification; Atmospheric modeling; Autocorrelation; Classification algorithms; Clustering algorithms; Clustering methods; Data mining; Electrical capacitance tomography; Kernel; Machine learning algorithms; Testing; Classification Accuracy; Clustering; Data Mining; F-Measure; WKM Algorithm; Weighted Kernel K-Means;

fLanguage

English

Publisher

ieee

Conference_Titel

Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the

Conference_Location

London

Print_ISBN

978-1-4244-4456-4

Electronic_ISBN

978-1-4244-4457-1

Type

conf

DOI

10.1109/ICADIWT.2009.5273893

Filename

5273893