Evolutionary Hierarchical Time Series Clustering

Authors:
Monica Chis;Crina Grosan
Affiliations:
Avram Iancu University, Romania;Babes-Bolyai University, Romania
Venue:
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Year:
2006

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Abstract

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.