A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Agents in Traffic Modelling - From Reactive to Social Behaviour
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Understanding Agent Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Acceleration schemes with application to the EM algorithm
Computational Statistics & Data Analysis
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
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The clustering model integrating Finite Mixture Model (FMM) and classical Expectation-Maximum (EM) algorithm has been applied to tropical cyclone (TC) tracks during the last decade. However, the efficiency of classical EM algorithm is insufficiently good and the robustness of the model is not verified. Besides, it is inconvenient for users to manually choose the parameters for the cluster analysis. In order to improve the efficiency of classical EM algorithm, the "Lazy-Ψα 2" EM is proposed by integrating Lazy EM algorithm and Ψα 2 algorithm. Sensitivity analysis is conducted to ensure the insensitivity of the clustering model to the amount of data set. The cluster analysis is implemented on an agent-based framework by which the tool can automatically choose the parameters by evaluating the clustering performance. TC tracks in western North Pacific from 1949 to 2006 are classified into 12 clusters by the probabilistic clustering model that is solved by "Lazy-Ψα 2" EM algorithm. The log-likelihood is taken as the performance indicator. Elaborate comparisons are made between the present cluster analysis and other cluster analyses related to TC tracks.