Abstract
A partitioning cluster method for mixed feature-type symbolic data is presented. This method needs a previous pre-processing step to transform Boolean symbolic data into modal symbolic data. The presented dynamic clustering algorithm has then as input a set of vectors of modal symbolic data (weight distributions) 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 synthetic symbolic data sets and applications with real symbolic data sets are considered.
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Cardoso Rodrigues de Souza, R.M., de Assis Tenorio de Carvalho, F., Pizzato, D.F. (2007). A Partitioning Method for Mixed Feature-Type Symbolic Data Using a Squared Euclidean Distance. In: Freksa, C., Kohlhase, M., Schill, K. (eds) KI 2006: Advances in Artificial Intelligence. KI 2006. Lecture Notes in Computer Science(), vol 4314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69912-5_20
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DOI: https://doi.org/10.1007/978-3-540-69912-5_20
Publisher Name: Springer, Berlin, Heidelberg
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