On clustering tree structured data with categorical nature

Pattern Recognition

Volumn 41, Issue 12, 2008, Pages 3613-3623

  Boutsinas, B ^a   Papastergiou, T ^a  

^a UNIVERSITY OF PATRAS   (Greece)

Author keywords

(Dis)similarity measures; Clustering; Data mining

Indexed keywords

CHLORINE COMPOUNDS; CLUSTER ANALYSIS; DATA PROCESSING; FLOW OF SOLIDS; NUMERICAL METHODS; TREES (MATHEMATICS);

(DIS)SIMILARITY MEASURES; ATTRIBUTE VALUES; CATEGORICAL ATTRIBUTES; CATEGORICAL DATA; CLUSTERING; CLUSTERING METHODS; CLUSTERING TREES; DATA MINING; K-MEANS CLUSTERING ALGORITHMS; NUMERICAL DATA; SCIENTIFIC DATABASES; SIMILARITY MEASURES;

CLUSTERING ALGORITHMS;

EID: 49449104635     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.05.023     Document Type: Article

References (46)

1. Aldenderfer M.S., and Blashfield R.K. Cluster analysis. Quantitative Applications in the Social Sciences (1984), SAGE Publications, London
2. Dubes R.C., and Jain A.K. Clustering methodologies in exploratory data analysis. Adv. Comput. 19 (1980) 113-228
3. Johnson S. Hierarchical clustering schemes. Phychometrika (1967) 241-254
4. Sokal R., and Michener C.D. A statistical method for evaluating systematic relationships. Univ. Kans. Sci. Bull. 38 (1958) 1409-1438
5. Jain A.K., and Dubes R.C. Algorithms for Clustering Data (1988), Prentice-Hall, Englewoods Cliffs, NJ
6. J.B. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematics Statistics and Probability, 1967, pp. 281-297.
7. Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discovery 2 (1998) 283-304
8. Ruspini E.H. A new approach to clustering. Inf. Control 15 (1969) 22-32
9. Vrahatis M.N., Boutsinas B., Alevizos P., and Pavlides G. The new k-windows algorithm for improving the k-means clustering algorithm. J. Complexity 18 (2002) 375-391
10. Anderberg M. Cluster Analysis for Applications (1973), Academic Press, NY
11. Selim S.Z., and Ismail M.A. k-means-type algorithms: a generalized convergence theorem and characterization of local optimality. IEEE Trans. Pattern Anal. Mach. Intell. 6 1 (1984) 81-87
12. L. Kaufman, P.J. Rousseeuw, Finding Groups in Data An Introduction to Cluster Analysis, Wiley Series in Probability and Mathematical Statistics, 1990.
13. Kodratoff Y. Introduction to Machine Learning (1988), Pitman, London
14. Bock H.H., and Diday E. Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data. Studies in Classification, Data Analysis, and Knowledge Organisation vol. 15 (2000), Springer, Berlin
15. Medin D.L., and Schaffer M.M. Context Theory of Classification Learning. Psychol. Rev. 85 3 (1978) 207-238
16. Greenacre M.J. Theory and Applications of Correspondence Analysis (1984), Academic Press, New York
17. B. Zhang, S.N. Srihari, Properties of binary vector dissimilarity measures, in: Proceedings of International Conference on Computer Vision, Pattern Recognition and Image Processing (CVPRIP), North Carolina, 2003.
18. Dunn-Rankin P. Scaling Methods (1983), Lawrence Erlbaum Associates
19. Cheng V., Li C.H., Kwok J.T., and Li C.K. Dissimilarity learning for nominal data. Pattern Recognition 37 7 (2004) 1471-1477
20. Cox T.F., and Cox M.A.A. Multidimensional Scaling (1994), Chapman & Hall, London
21. Kruskal J.B. Nonmetric multidimensional scaling: a numerical method. Psychometrika 36 (1964) 115-129
22. D. Malerba, F. Esposito, V. Gioviale, V. Tamma, Comparing dissimilarity measures for symbolic data analysis, in: Proceedings of the Joint Conferences on "New Techniques and Technologies for Statistics" and "Exchange of Technology and Know-how", 2001, pp. 473-481.
23. Esposito F., Malerba D., and Semeraro G. Classification in noisy environments using a distance measure between structural symbolic descriptions. IEEE Trans. Pattern Anal. Mach. Intell. 14 3 (1992) 390-402
24. Fisher D.H. Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2 (1987) 139-172
25. Michalski R.S., and Stepp R.E. Learning from observation: conceptual clustering. In: Michalski R.S., Carbonell J.G., and Mitchell T.M. (Eds). Machine Learning: An Artificial Intelligence Approach vol. 1 (1983), Tioga Publishing, Palo Alto, CA
26. Rada R., Mili H., Bicknell E., and Blettner M. Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19 1 (1989) 17-30
27. Quillian M.R. Semantic memory. In: Minsky M. (Ed). Semantic Information Processing (1968), MIT Press, Cambridge, MA
28. J.J. Jiang, D.W. Conrath, Semantic similarity based on corpus statistics and lexical taxonomy, in: Proceedings of International Conference on Research in Computational Linguistics, 1997.
29. M. Sussna, Word sense disambiguation for free-text indexing using a massive semantic network, in: Proceedings of the Second International Conference on Information and Knowledge Management, 1993, pp. 67-74.
30. E. Blanchard, M. Harzallah, H. Briand, P. Kuntz, A typology of ontology-based semantic measures, in: Open Interop Workshop on Enterpise Modelling and Ontologies for Interoperability, 2005.
31. M. Warin, H. Oxhammar, M. Volk, Enriching an ontology with wordnet based on similarity measures, in: Proceedings of the MEANING-2005 Workshop, Trento, 2005.
32. Leacock C., and Chodorow M. Combining local context and wordnet similarity for word sense identification. In: Fellbaum C. (Ed). WordNet: An Electronic Lexical Database (1998), MIT Press, Cambridge, MA 265-283
33. Z. Wu, M. Palmer, Verb semantics and lexical selection, in: Proceedings of the 32nd Annual Meeting of the Associations for Computational Linguistics, 1994, pp. 133-138.
34. P. Resnik, Using information content to evaluate semantic similarity in a taxonomy, in: Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 1, 1995, pp. 448-453.
35. Lin D. An information-theoretic definition of similarity. Proceedings of the 15th International Conference on Machine Learning (1998), Morgan Kaufmann, Los Atlos, CA 296-304
36. Hirst G., and StOnge D. Lexical chains as representation of context for the detection and correction of malaproprisms. In: Fellbaum C. (Ed). WordNet. An electronic lexical database (1998), MIT Press, Cambridge, MA 305-332
37. Boutsinas B., Tasoulis D., and Vrahatis M.N. An efficient algorithm for estimating the number of clusters using a windowing technique. Pattern Recognition Image Anal. 16 2 (2006) 143-154
38. C. Berge, Graphs, North-Holland Mathematical Library, vol. 6, part 1, second ed., 1985, pp. 72-73.
39. I. Niles, A. Pease, Towards a standard upper ontology, in: Second International Conference on Formal Ontology in Information Systems, Ogunquit, Maine, 2001.
40. Lenat D.B. Cyc: a large-scale investment in knowledge infrastructure. Commun. ACM 38 11 (1995)
41. Cover T., and Hart P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13 1 (1967) 21-27
42. Kalfoglou Y., and Schorlemmer M. Ontology mapping: the state of the art. Knowl. Eng. Rev. 18 1 (2003)
43. H. Wache, T. Vogele, U. Visser, H. Stuckenschmidt, G. Schuster, H. Neumann, S. Hubner, Ontology-based integration of information-a survey of existing approaches, in: IJCAI Workshop: Ontologies and Information Sharing, Seattle, WA, 2001.
44. B.T. Dai, N. Koudas, B.C. Ooi, D. Srivastava, S. Venkatasubramanian, Column Heterogeneity as a Measure of Data Quality.
45. T. Johnson, T. Dasu, Data quality and data cleaning: an overview, in: SIGMOD, Tutorial, 2003.
46. B. Boutsinas, S. Athanasiadis, On merging classification rules, Int. J. Inf. Technol. Decision Making (2008).