Abstract
Finding a linear structure in multidimensional data is a main purpose of the principal component analysis (PCA). This paper describes a feature clustering method to detect monotonic chain structures embedded in symbolic data tables based on the Cartesian system model (CSM) which is a mathematical model to manipulate symbolic objects.
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Ichino, M. (2007). Feature Clustering Method to Detect Monotonic Chain Structures in Symbolic Data. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_9
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DOI: https://doi.org/10.1007/978-3-540-73560-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73558-8
Online ISBN: 978-3-540-73560-1
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