Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Extraction and Clustering of Motion Trajectories in Video
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Dynamical Gaussian mixture model for tracking elliptical living objects
Pattern Recognition Letters
Introduction to Information Retrieval
Introduction to Information Retrieval
OIF - An Online Inferential Framework for Multi-object Tracking with Kalman Filter
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Detecting changes of clustering structures using normalized maximum likelihood coding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Where are we going? predicting the evolution of individuals
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning of models upon the primary objects (patients, customers) rather than the transactions, especially when these models are subjected to drift. We address this problem by combining advances of online clustering on multivariate data with the trajectory mining paradigm. We model the measurements of each individual primary object (e.g. its transactions), taken at irregular time intervals, as a trajectory in a high-dimensional feature space. Then, we cluster individuals with similar trajectories to identify sub-populations that evolve similarly, e.g. groups of customers that evolve similarly or groups of employees that have similar careers. We assume that the multivariate trajectories are generated by drifting Gaussian Mixture Models. We study (i) an EM-based approach that clusters these trajectories incrementally as a reference method that has access to all the data for learning, and propose (ii) an online algorithm based on a Kalman filter that efficiently tracks the trajectories of Gaussian clusters. We show that while both methods approximate the reference well, the algorithm based on a Kalman filter is faster by one order of magnitude compared to the EM-based approach.