Awan, A. Majid and Md Sap, Mohd Noor (2005) Clustering Spatial Data Using a Kernel-Based Algorithm. In: Postgraduate Annual Research Seminar 2005.
This paper presents a method for unsupervised partitioning of data using kernel methods which offer strength to deal with complex data non-linearly separable in input space. This work gets inspiration from the notion that a non-linear data transformation into some high dimensional feature space increases the possibility of linear separability of the patterns in the transformed space. Therefore, it simplifies exploration of the associated structure in the data. Kernel methods implicitly perform a non-linear mapping of the input data into a high dimensional feature space by replacing the inner products with an appropriate positive definite function. Firstly, in this paper, selective kernel-based clustering techniques are analyzed and their shortcomings are identified especially for spatial data analysis. Finally, we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering spatial data as a case study. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data. Therefore, this work comes up with new clustering algorithm using kernel-based methods for effective and efficient data analysis by exploring structures in the data.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||Clustering algorithms, K-means, Kernel methods, spatial data, unsupervised learning|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System|
|Deposited By:||Ms Haslina Hashim|
|Deposited On:||24 May 2007 00:45|
|Last Modified:||01 Jun 2010 03:09|
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