Sequence Mining for Business Analytics: Building Project Taxonomies for Resource Demand Forecasting
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
Using semi-parametric clustering applied to electronic health record time series data
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
DTW based clustering to improve hand gesture recognition
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
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We introduce an approach for model-based sequence clustering that addresses several drawbacks of existing algorithms. The approach uses a combination of Hidden Markov Models (HMMs) for sequence estimation and Dynamic Time Warping (DTW) for hierarchical clustering, with interlocking steps of model selection, estimation and sequence grouping. We demonstrate experimentally that the algorithm can effectively handle sequences of widely varying lengths, unbalanced cluster sizes, as well as outliers.