Path-Based Dominant-Set Clustering

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

Volumn 9279, Issue , 2015, Pages 150-160

  Zemene, Eyasu ^a   Pelillo, Marcello ^a  

^a CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; GRAPH THEORY; IMAGE ANALYSIS; IMAGE PROCESSING;

BACKGROUND NOISE; CLUSTERING METHODS; CLUTTERED BACKGROUNDS; DOMINANT SET CLUSTERING; EDGE-WEIGHTED GRAPH; NOISE CONDITIONS; SIMILARITY MEASURE; SPECTRAL GRAPH THEORY;

CLUSTERING ALGORITHMS;

EID: 84944729216     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-23231-7_14     Document Type: Conference Paper

References (15)

1. Chang, H., Yeung, D.: Robust path-based spectral clustering. Pattern Recognition 41(1), 191-203 (2008)
2. Chehreghani, M. H.: Information-Theoretic Validation of Clustering Algorithms. Ph. D. thesis, ETH ZURICH (2013)
3. Fischer, B., Buhmann, J. M.: Bagging for path-based clustering. IEEE Trans. Pattern Anal. Mach. Intell. 25(11), 1411-1415 (2003a)
4. Fischer, B., Buhmann, J. M.: Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(4), 513-518 (2003b)
5. Fischer, B., Buhmann, J. M.: Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(4), 513-518 (2003c)
6. Lichman, M.: UCI machine learning repository (2013). http://archive. ics. uci. edu/ml
7. Ng, A. Y., Jordan, M. I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849-856. MIT Press (2001)
8. Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Machine Intell. 29(1), 167-172 (2007)
9. Pelillo, M.: What is a cluster? perspectives from game theory. In: Proc. of the NIPS Workshop on Clustering Theory (2009)
10. Rota Bulò, S., Pelillo, M.: A game-theoretic approach to hypergraph clustering. IEEE Trans. Pattern Anal. Machine Intell. 35(6), 1312-1327 (2013)
11. Rota Bulò, S., Pelillo, M., Bomze, I. M.: Graph-based quadratic optimization: A fast evolutionary approach. Computer Vision and Image Understanding 115(7), 984-995 (2011)
12. Rota Bulò, S., Torsello, A., Pelillo, M.: A game-theoretic approach to partial clique enumeration. Image Vision Comput. 27(7), 911-922 (2009)
13. Torsello, A., Rota Bulò, S., Pelillo, M.: Grouping with asymmetric affinities: a game-theoretic perspective. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 292-299 (2006)
14. Torsello, A., Rota Bulò, S., Pelillo, M.: Beyond partitions: allowing overlapping groups in pairwise clustering. In: 19th International Conference on Pattern Recognition (ICPR 2008), December 8-11, 2008, Tampa, Florida, USA, pp. 1-4 (2008)
15. Zelnik-manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems 17, pp. 1601-1608. MIT Press (2004)