Abstract
In this paper we extend the well known principal component analysis (PCA) to deal with Boolean symbolic objects (SO’s). We generalize the procedure by (1994) by means of the dissimilarity functions introduced by (1999). In this way we extend PCA to deal with constrained SO’s. In addition we propose to extend the above procedure to deal with three-way SO’s applying three-way methods such as Tucker3 and Parafac. Finally, a visualization procedure that takes into account the SO’s complexity is suggested.
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Giordani, P. (2003). Principal Component Analysis of Boolean Symbolic Objects. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_25
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DOI: https://doi.org/10.1007/978-3-642-18991-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40354-8
Online ISBN: 978-3-642-18991-3
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