Spatiotemporal Clustering from Noisy Data

Kanjilal, P., Das, S., and Lawless, D.

IJCAI Workshop on Spatial and Temporal Reasoning, Hyderabad, India (January, 2007)

Spatiotemporal clustering of multiple objects in a simulated space is considered. New preprocessing methods using Singular Value Decomposition (SVD) and Approximate Entropy are discussed to reduce clutter. A systematic procedure is presented to identify spatiotemporal clusters in terms of optimal sets of singular vectors. A novel orthogonal transformation based approach is proposed for initializing the k-mean square clusters.

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