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Cluster Analysis for the Statistical Modeling of Aesthetic Judgment Data Related to Comics Artists
Abstract:We compare three categorical data clustering algorithms with respect to the problem of classifying cultural data related to the aesthetic judgment of comics artists. Such a classification is very important in Comics Art theory since the determination of any classes of similarities in such kind of data will provide to art-historians very fruitful information of Comics Art-s evolution. To establish this, we use a categorical data set and we study it by employing three categorical data clustering algorithms. The performances of these algorithms are compared each other, while interpretations of the clustering results are also given.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084686Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1319
 P. Machado, and A. Cardoso, Computing aesthetics, Lecture Notes in Artificial Intelligence, 1515, 1998, 219-228.
 S. Baluja, D. Pomerlau, and J. Todd, Towards automated artificial evolution for computer-generated images, Connection Science, 6(2), 1994, 325-354.
 M. Davenport, and G. Studdert-Kennedy, The statistical analysis of aesthetic judgment: an exploration, Applied Statistics, 21, 1972, 324- 332.
 B. S. Everitt, Cluster analysis, 3rd Edition, Arnold Publications, N.Y., 1993.
 G. E. Tsekouras, A. Kaoua, and E. Sampanikou, Potential-based fuzzy clustering and cluster validity for categorical data and its application in modeling cultural data, 3rd IEEE Conference on Computational Cybernetics, Mauritious, May 2005, 73-78.
 M. Graves, Test of Drawing Appreciation, The Psychological Corporation, 1977.
 S. Guha, R. Rastogi, and K. Shim, ROCK: A robust clustering algorithm foa categorical attributes, Information Systems, 25(5), 2000, 345-366.
 K. Mali, & M. Sushmita, Clustering of symbolic data and its validation, Lecture Notes in Artificial Intelligence, 2275, 2002, 339-344.
 G. E. Tsekouras, D. Papageorgiou, S. B. Kotsiantis, C. Kalloniatis, and P. Pintelas, A fuzzy logic-based approach for detecting shifting patterns in cross-cultural data, Lecture Notes in Artificial Intelligence, 3533, 2005, 705-708.
 T. Morzy, M. Wojciechowski, and M. Zakrzewicz, Scalable hierarchical clustering method for sequences of categorical values, Lecture Notes in Artificial Intelligence, 2035, 2001, 282-293.
 Z. Huang, Extensions of the k-means algorithm for clustering large data sets with categorical values, Data Mining and Knowledge Discovery, 2, 1998, 283-304.
 Z. Huang, & M. K. Ng, A fuzzy k-modes algorithm for clustering categorical data, IEEE Transactions on Fuzzy Systems, 7(4), 1999, 446- 452.