{"title":"Cluster Analysis for the Statistical Modeling of Aesthetic Judgment Data Related to Comics Artists","authors":"George E. Tsekouras, Evi Sampanikou","volume":12,"journal":"International Journal of Humanities and Social Sciences","pagesStart":826,"pagesEnd":829,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/15253","abstract":"We compare three categorical data clustering\r\nalgorithms with respect to the problem of classifying cultural data\r\nrelated to the aesthetic judgment of comics artists. Such a\r\nclassification is very important in Comics Art theory since the\r\ndetermination of any classes of similarities in such kind of data will\r\nprovide to art-historians very fruitful information of Comics Art-s\r\nevolution. To establish this, we use a categorical data set and we\r\nstudy it by employing three categorical data clustering algorithms.\r\nThe performances of these algorithms are compared each other,\r\nwhile interpretations of the clustering results are also given.","references":"[1] P. Machado, and A. Cardoso, Computing aesthetics, Lecture Notes in\r\nArtificial Intelligence, 1515, 1998, 219-228.\r\n[2] S. Baluja, D. Pomerlau, and J. Todd, Towards automated artificial\r\nevolution for computer-generated images, Connection Science, 6(2),\r\n1994, 325-354.\r\n[3] M. Davenport, and G. Studdert-Kennedy, The statistical analysis of\r\naesthetic judgment: an exploration, Applied Statistics, 21, 1972, 324-\r\n332.\r\n[4] B. S. Everitt, Cluster analysis, 3rd Edition, Arnold Publications, N.Y.,\r\n1993.\r\n[5] G. E. Tsekouras, A. Kaoua, and E. Sampanikou, Potential-based fuzzy\r\nclustering and cluster validity for categorical data and its application in\r\nmodeling cultural data, 3rd IEEE Conference on Computational\r\nCybernetics, Mauritious, May 2005, 73-78.\r\n[6] M. Graves, Test of Drawing Appreciation, The Psychological\r\nCorporation, 1977.\r\n[7] S. Guha, R. Rastogi, and K. Shim, ROCK: A robust clustering algorithm\r\nfoa categorical attributes, Information Systems, 25(5), 2000, 345-366.\r\n[8] K. Mali, & M. Sushmita, Clustering of symbolic data and its validation,\r\nLecture Notes in Artificial Intelligence, 2275, 2002, 339-344.\r\n[9] G. E. Tsekouras, D. Papageorgiou, S. B. Kotsiantis, C. Kalloniatis, and\r\nP. Pintelas, A fuzzy logic-based approach for detecting shifting patterns\r\nin cross-cultural data, Lecture Notes in Artificial Intelligence, 3533,\r\n2005, 705-708.\r\n[10] T. Morzy, M. Wojciechowski, and M. Zakrzewicz, Scalable hierarchical\r\nclustering method for sequences of categorical values, Lecture Notes in\r\nArtificial Intelligence, 2035, 2001, 282-293.\r\n[11] Z. Huang, Extensions of the k-means algorithm for clustering large data\r\nsets with categorical values, Data Mining and Knowledge Discovery, 2,\r\n1998, 283-304.\r\n[12] Z. Huang, & M. K. Ng, A fuzzy k-modes algorithm for clustering\r\ncategorical data, IEEE Transactions on Fuzzy Systems, 7(4), 1999, 446-\r\n452.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 12, 2007"}