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Evolutionary Hierarchical Time Series Clustering
Jinan, China October 16-October 18
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISDA.2006.144Sixth International Conference on Int ...
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Monica Chis, Avram Iancu University, Romania
Crina Grosan, Babes-Bolyai University, Romania
Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics. In this paper a new evolutionary algorithm for detecting the hierarchical structure of an input time series data set is proposed. A new linear representation of the cluster structure within the data set is used. Proposed algorithm uses mutation and crossover as (search) variation operators. A new fitness function is proposed.
Citation:
Monica Chis, Crina Grosan, "Evolutionary Hierarchical Time Series Clustering," isda, vol. 1, pp.451-455, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 1, 2006
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