Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
A universal construction of Artstein's theorem on nonlinear stabilization
Systems & Control Letters
Deterministic annealing, clustering, and optimization
Deterministic annealing, clustering, and optimization
Vector quantization and signal compression
Vector quantization and signal compression
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Clustering Algorithms
Constrained Clustering as an Optimization Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fuzzy clustering-based approach to automatic freeway incident detection and characterization
Fuzzy Sets and Systems - Clustering and modeling
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discrete & Computational Geometry
Clustering moving objects for spatio-temporal selectivity estimation
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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In this paper, we consider the general class of coverage and clustering problems in a dynamic environment, and propose a computationally efficient framework to address them. We define the problem of achieving instantaneous coverage as a combinatorial optimization problem in a Maximum Entropy Principle framework. We then extend the framework to a dynamic environment, thereby allowing us to address the inherent trade-off between the resolution of the clusters and the computation cost, and provides flexibility to a variety of dynamic specifications. The proposed framework addresses both the coverage as well as tracking aspects of the problem. The determination of cluster centers and their associated velocity field is cast as a control design problem ensuring that the algorithm achieves progressively better coverage with time. Simulation results presented in the paper demonstrate that the proposed algorithm achieves target coverage costs five to seven times faster than related frame-by-frame methods, with the additional ability to identify natural clusters in the dataset.