Density based clustering via dominant sets

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

Volumn 9896 LNAI, Issue , 2016, Pages 80-91

  Hou, Jian ^a,b   Liu, Weixue ^a   Xu, E ^a  

^a BOHAI UNIVERSITY   (China)

^b CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

Author keywords

Cluster extension; Density based clustering; Dominant set; Histogram equalization

Indexed keywords

EQUALIZERS; GRAPHIC METHODS; NEURAL NETWORKS; PARAMETER ESTIMATION; PATTERN RECOGNITION;

CLUSTER EXTENSION; CLUSTERING RESULTS; DENSITY-BASED CLUSTERING; DENSITY-BASED CLUSTERING ALGORITHMS; DOMINANT SET; HISTOGRAM EQUALIZATIONS; PARAMETER-TUNING; STATE-OF-THE-ART ALGORITHMS;

CLUSTERING ALGORITHMS;

EID: 84987933829     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46182-3_7     Document Type: Conference Paper

References (16)

1. Brendan, J.F., Delbert, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
2. Bulo, S.R., Torsello, A., Pelillo, M.: A game-theoretic approach to partial clique enumeration. Image Vis. Comput. 27(7), 911–922 (2009)
3. Daszykowski, M., Walczak, B., Massart, D.L.: Looking for natural patterns in data: Part 1. Density-based approach. Chemom. Intell. Lab. Syst. 56(2), 83–92 (2001)
4. Ester, M., Kriegel, H.P., Sander, J., Xu, X.W.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
5. Fu, L., Medico, E.: Flame, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinform. 8(1), 1–17 (2007)
6. Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1(1), 1–30 (2007)
7. Jain, A.K., Law, M.H.C.: Data clustering: a user’s dilemma. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 1–10. Springer, Heidelberg (2005)
8. Pavan, M., Pelillo, M.: Efficient out-of-sample extension of dominant-set clusters. In: Advances in Neural Information Processing Systems, pp. 1057–1064 (2005)
9. Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 167–172 (2007)
10. Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)
11. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 167–172 (2000)
12. Tripodi, R., Pelillo, M.: Document clustering games. In: The 5th International Conference on Pattern Recognition Applications and Methods, pp. 109–118 (2016)
13. Veenman, C.J., Reinders, M., Backer, E.: A maximum variance cluster algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1273–1280 (2002)
14. Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20(1), 68–86 (1971)
15. Zemene, E., Pelillo, M.: Path-based dominant-set clustering. In: Murino, V., Puppo, E. (eds.) ICIAP 2015. LNCS, vol. 9279, pp. 150–160. Springer, Heidelberg (2015)
16. Zhu, X., Loy, C.C., Gong, S.: Constructing robust affinity graphs for spectral clustering. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1450–1457 (2014)