Abstract
In this paper we consider the approach proposed in the hierarchical clustering method MIXCLAS to obtain a new non-hierarchical method which we named MIXISO. This method can analyze mixed mode data with a large number of units including in the presence of missing data. We demonstrate the application of this method on artificial and real data-set with missing values.
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© 2001 Springer-Verlag Berlin Heidelberg
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Di Ciaccio, A. (2001). MIXISO: a Non-Hierarchical Clustering Method for Mixed-Mode Data. In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_4
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DOI: https://doi.org/10.1007/978-3-642-59471-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41488-9
Online ISBN: 978-3-642-59471-7
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