Dynamic clustering of interval data using a Wasserstein-based distance

Pattern Recognition Letters

Volumn 29, Issue 11, 2008, Pages 1648-1658

  Irpino, Antonio ^a   Verde, Rosanna ^a  

^a SECOND UNIVERSITY OF NAPLES   (Italy)

Author keywords

Clustering; Inertia; Interval data; Wasserstein distance

Indexed keywords

CLUSTERING ALGORITHMS; DYNAMICS; FLOW OF SOLIDS; MECHANICS; SET THEORY;

(PL) PROPERTIES; CLUSTERING TECHNIQUES; DYNAMIC CLUSTERING; DYNAMIC CLUSTERING ALGORITHM; ELSEVIER (CO); INTERVAL DATA; METRICS (CO); REAL DATA; STATISTICAL UNITS;

CLUSTER ANALYSIS;

EID: 45449099044     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.04.008     Document Type: Article

References (18)

1. Bertoluzza C., Corral N., and Salas A. On a new class of distances between fuzzy numbers. Mathware Soft Comput. 2 (1995) 71-84
2. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
3. Bock H.H. Analysis of Symbolic Data, Exploratory Methods for Extracting Statistical Information from Complex Data. Studies in Classification, Data Analysis and Knowledge Organisation (2000), Springer-Verlag
4. Chavent M., De Carvalho F., Lechevallier Y., and Verde R. Trois nouvelles méthodes de classification automatique de données symboliques de type intervalle. Rev. Stat. Appl. 4 (2003) 529
5. De Carvalho F. Proximity coefficients between Boolean symbolic objects. Proc. 4th Conf. on International Federation of Classification Societies. New Approaches in Classification and Data Analysis (1998), Springer 370-378
6. De Carvalho F. Extension based proximities between constrained Boolean symbolic objects. Proc. 5th Conf. on International Federation of Classification Societies. Data Science, Classification and Related Methods (1998), Springer 387-394
7. 2 distance. Comput. Statist. 21 2 (2006) 231-250
8. Diday E. Le méthode des nuées dynamique. Rev. Statist. Appl. 19 2 (1971) 19-34
9. Gibbs A.L., and Su F.E. On choosing and bounding probability metrics. Internat. Statist. Rev. 70 (2002) 419
10. Gowda K.C., and Diday E. Symbolic clustering using a new dissimilarity measure. Pattern Recognition 24 (1991) 567-578
11. Gowda K.C., and Ravi T.V. Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity. Pattern Recognition Lett. 16 (1995) 647-652
12. Hubert L., and Arabie P. Comparing partitions. J. Classif. 2 (1985) 193-218
13. Ichino M., and Yaguchi H. Generalized Minkowsky metrics for mixed feature-type data analysis. IEEE Trans. System Man Cybernet. 24 4 (1994) 698-708
14. Irpino A., and Romano E. Optimal histogram representation of large data sets: Fisher vs piecewise linear approximations. RNTI E-9 (2007) 99-110
15. Kodratoff Y., and Bisson G. The epistemology of conceptual clustering: Kbg, an implementation. J. Intell. Inform. Systems 1 1 (1992) 5784
16. Michalski R., Stepp R., and Diday E. A Recent Advance in Data Analysis: Clustering Objects into Classes Characterized by Conjunctive Concepts vol. 1 (1981), North-Holland, New York p. 3356
17. Tran L., and Duckstein L. Comparison of fuzzy numbers using a fuzzy distance measure. Fuzzy Sets Systems 130 (2002) 331-341
18. Verde, R., Lauro, N., 2000. Basic choices and algorithms for symbolic objects dynamical clustering. In: XXXIIe Journées de Statistique, Fés, Maroc, Societé Française de Statistique, p. 3842.