An exploration of climate data using complex networks
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
An exploration of climate data using complex networks
ACM SIGKDD Explorations Newsletter
Kernel-based algorithm for clustering spatial data
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
An intelligent system based on kernel methods for crop yield prediction
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining robust neighborhoods for quality control of sensor data
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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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.