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In this article, we present a new quantification method to realize the principal component analysis (PCA) for symbolic data tables. We first describe the nesting property for the monotone point sequences and the correlation matrix by the rank correlation coefficient. Then, we present the object splitting method by which interval valued data table can be(More)
In this paper, a simple and efficient feature selection scheme for symbolic data is proposed. The proposed scheme exploits the symbolic multivalued proximity measures for feature selection. The effectiveness of the proposed scheme has been demonstrated through experiments on standard symbolic data sets