Sap, M.N Md and Awan, A.M (2005) Finding spatio-temporal patterns in climate data using clustering. In: Proceedings - 2005 International Conference on Cyberworlds, CW 2005, 23th -25th Nov. 2005.
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Official URL: http://dx.doi.org/10.1109/CW.2005.45
This paper presents a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate 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. In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Maznira Sylvia Azra Mansor|
|Deposited On:||09 Jan 2009 02:05|
|Last Modified:||01 Jun 2010 15:53|
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