Abstract
Cluster analysis is to be included among the favorite data mining techniques. Cluster analysis of time series has received great attention only recently mainly because of the several difficult issues involved. Among several available methods, genetic algorithms proved to be able to handle efficiently this topic. Several partitions are considered and iteratively selected according to some adequacy criterion. In this artificial “struggle for survival” partitions are allowed to interact and mutate to improve and produce a “high quality” solution. Given a set of time series two genetic algorithms are considered for clustering (the number of clusters is assumed unknown). Both algorithms require a model to be fitted to each time series to obtain model parameters and residuals. These methods are applied to a real data set concerned with the visitors flow recorded, in state owned museums with paid admission, in the Lazio region of Italy.
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Baragona, R., Vitrano, S. (2006). Genetic Algorithms-based Approaches for Clustering Time Series. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_1
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DOI: https://doi.org/10.1007/3-540-35978-8_1
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