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Clustering-Inverse: A Generalized Model for Pattern-Based Time Series Segmentation
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Journal of Intelligent Learning Systems and Applications·Volume 03 (2011)·Pages 26–36·Published 24 February 2011·DOI10.4236/jilsa.2011.31004
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Abstract
Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.
KeywordsPattern-Based Time Series SegmentationClustering-InverseDynamic Time WarpingPerceptually Important PointsEvolution ComputationParticle Swarm OptimizationGenetic Algorithm
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