Abstract
A dynamic clustering method for mixed feature-type symbolic data is presented. The proposed method needs a previous pre-processing step to transform Boolean symbolic data into modal symbolic data. The presented dynamic clustering method has then as input a set of vectors of modal symbolic data and furnishes a partition and a prototype to each class by optimizing an adequacy criterion based on a suitable squared Euclidean distance. To show the usefulness of this method, examples with symbolic data sets are considered.
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de Souza, R.M.C.R., de Carvalho, F.d.A.T., Pizzato, D.F. (2006). A Dynamic Clustering Method for Mixed Feature-Type Symbolic Data. In: Batagelj, V., Bock, HH., Ferligoj, A., Žiberna, A. (eds) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-34416-0_22
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DOI: https://doi.org/10.1007/3-540-34416-0_22
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