A graph-based clustering method and its applications

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4729 LNCS, Issue , 2007, Pages 277-287

  Foggia, Pasquale ^a   Percannella, Gennaro ^b   Sansone, Carlo ^a   Vento, Mario ^b  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b UNIVERSITY OF SALERNO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; COMPUTER APPLICATIONS; COMPUTER VISION; CYBERNETICS; FLOW OF SOLIDS; GRAPH THEORY; IMAGE PROCESSING;

COMPUTER VISION APPLICATIONS; GAUSSIAN; GRAPH-BASED CLUSTERING; INTERNATIONAL SYMPOSIUM; NUMBER OF CLUSTERS; SIZE AND SHAPE; SPHERICAL DISTRIBUTIONS; STATE-OF-THE-ART ALGORITHMS;

CLUSTER ANALYSIS;

EID: 49949095186     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-75555-5_26     Document Type: Conference Paper

References (15)

1. Jain, A.K., Murty, M.N., Flynn, PJ.: Data clustering: a review. ACM Computing Surveys 31(3), 264-323 (1999)
2. Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA (1988)
3. Kohonen, T.: Self-organizing maps. Springer, Heidelberg (1995)
4. Juszczak, P.: Learning to recognise. A study on one-class classification and active learning, PhD thesis, Delft University of Technology (2006), ISBN: 978-90-9020684-4
5. Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36, 2967-2991 (2003)
6. Wu, Z., Leahy, R.: An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation. IEEE Trans. on PAMI 15(11), 1101-1113 (1993)
7. Zahn, C.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers C-20, 68-86 (1971)
8. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
9. van Dongen, S.M.: Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht (2000)
10. Kannan, R., Vampala, S., Vetta, A.: On Clustering: Good, Bad and Spectral. In: Foundations of Computer Science 2000, pp. 367-378 (2000)
11. Gaertler, M.: Clustering with spectral methods. Master's thesis, Universität Konstanz (2002)
12. De Santo, M., Percannella, G., Sansone, C., Vento, M.: Combining experts for anchorperson shot detection in news videos. Pattern Analysis and Applications 7(4), 447-460 (2005)
13. Horowitz, E., Sahni, S.: Fundamentals of Computer Algorithms. Computer Science Press (1978)
14. Brandes, U., Gaertler, M., Wagner, D.: Experiments on Graph Clustering Algorithms. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 568-579. Springer, Heidelberg (2003)
15. Sajda, P., Spence, C., Pearson, J.: Learning contextual relationships in mammograms using a hierarchical pyramid neural network. IEEE Trans. on Medical Imaging 21(3), 239-250 (2002)