Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Stream prediction using a generative model based on frequent episodes in event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Multiple Interrelated Streams for Incremental Clustering
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Tree induction over perennial objects
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Summarizing cluster evolution in dynamic environments
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Online clustering of high-dimensional trajectories under concept drift
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Adaptive Windowing for Online Learning from Multiple Inter-related Data Streams
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
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When searching for patterns on data streams, we come across perennial (dynamic) objects that evolve over time. These objects are encountered repeatedly and each time with different definition and values. Examples are (a) companies registered at stock exchange and reporting their progress at the end of each year, and (b) students whose performance is evaluated at the end of each semester. On such data, domain experts also pose questions on how the individual objects will evolve: would it be beneficial to invest in a given company, given both the company's individual performance thus far and the drift experienced in the model? Or, how will a given student perform next year, given the performance variations observed thus far? While there is much research on how models evolve/change over time [Ntoutsi et al., 2011a], little is done to predict the change of individual objects when the states are not known a priori. In this work, we propose a framework that learns the clusters to which the objects belong at each moment, uses them as ad hoc states in a state-transition graph, and then learns a mixture model of Markov Chains, which predicts the next most likely state/cluster per object. We report on our evaluation on synthetic and real datasets.