Cluster Analysis for the Statistical Modeling of Aesthetic Judgment Data Related to Comics Artists

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.

Authors:

• George E. Tsekouras
• Evi Sampanikou

Keywords:

• Aesthetic judgment
• comics artists
• cluster analysis
• categorical data.

References:

[1] P. Machado, and A. Cardoso, Computing aesthetics, Lecture Notes in
Artificial Intelligence, 1515, 1998, 219-228.
[2] S. Baluja, D. Pomerlau, and J. Todd, Towards automated artificial
evolution for computer-generated images, Connection Science, 6(2),
1994, 325-354.
[3] M. Davenport, and G. Studdert-Kennedy, The statistical analysis of
aesthetic judgment: an exploration, Applied Statistics, 21, 1972, 324-
332.
[4] B. S. Everitt, Cluster analysis, 3rd Edition, Arnold Publications, N.Y.,
1993.
[5] 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.
[6] M. Graves, Test of Drawing Appreciation, The Psychological
Corporation, 1977.
[7] S. Guha, R. Rastogi, and K. Shim, ROCK: A robust clustering algorithm
foa categorical attributes, Information Systems, 25(5), 2000, 345-366.
[8] K. Mali, & M. Sushmita, Clustering of symbolic data and its validation,
Lecture Notes in Artificial Intelligence, 2275, 2002, 339-344.
[9] 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.
[10] 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.
[11] 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.
[12] Z. Huang, & M. K. Ng, A fuzzy k-modes algorithm for clustering
categorical data, IEEE Transactions on Fuzzy Systems, 7(4), 1999, 446-
452.