Abstract
As an important unsupervised learning approach, clustering is widely used in pattern recognition, information retrieval and image analysis, etc. In various clustering approaches, graph based clustering has received much interest and obtain impressive success in application recently. However, existing graph based clustering algorithms usually require as input some parameters in one form or another. In this paper we study the dominant sets clustering algorithm and present a new clustering algorithm without any parameter input. We firstly use histogram equalization to transform the similarity matrices of data. This transformation is shown to make the clustering results invariant to similarity parameters effectively. Then we merge clusters based on the ratio between intra-cluster and inter-cluster similarity. Our algorithm is shown to be effective in experiments on seven datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data. In: International Conference on Knowledge Discovery and Data Mining, pp. 517–521 (2005)
Panagiotakis, C., Grinias, I., Tziritas, G.: Natural image segmentation based on tree equipartition, bayesian flooding and region merging. IEEE Trans. Image Process. 20, 2276–2287 (2011)
Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest. In: IEEE International Conference on Computer Vision, pp. 731–738 (2009)
Panagiotakis, C., Papadakis, H., Grinias, E., Komodakis, N., Fragopoulou, P., Tziritas, G.: Interactive image segmentation based on synthetic graph coordinates. Pattern Recogn. 46, 2940–2952 (2013)
Zhao, Y., Nie, X., Duan, Y., Huang, Y., Luo, S.: A benchmark for interactive image segmentation algorithms. In: IEEE Workshop on Person-Oriented Vision, 33–38 (2011)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 167–172 (2000)
Brendan, J.F., Delbert, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Mach. Intell. 29, 167–172 (2007)
Hou, J., Xu, E., Liu, W.X., Xia, Q., Qi, N.M.: A density based enhancement to dominant sets clustering. IET Comput. Vision 7, 354–361 (2013)
Yang, X.W., Liu, H.R., Laecki, L.J.: Contour-based object detection as dominant set computation. Pattern Recogn. 45, 1927–1936 (2012)
Hou, J., Pelillo, M.: A simple feature combination method based on dominant sets. Pattern Recogn. 46, 3129–3139 (2013)
Hamid, R., Maddi, S., Johnson, A.Y., Bobick, A.F., Essa, I.A., Isbell, C.: A novel sequence representation for unsupervised analysis of human activities. Artif. Intell. 173, 1221–1244 (2009)
Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Mach. Learn. 56, 89–113 (2004)
Hou, J., Xu, E., Chi, L., Xia, Q., Qi, N.M.: Dset++: a robust clustering algorithm. In: International Conference on Image Processing, pp. 3795–3799 (2013)
Pavan, M., Pelillo, M.: A graph-theoretic approach to clustering and segmentation. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 145–152 (2003)
Rota Bulò, S., Pelillo, M., Bomze, I.M.: Graph-based quadratic optimization: a fast evolutionary approach. Comput. Vis. Image Underst. 115, 984–995 (2011)
Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1, 1–30 (2007)
Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20, 68–86 (1971)
Chang, H., Yeung, D.Y.: Robust path-based spectral clustering. Pattern Recogn. 41, 191–203 (2008)
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)
Fu, L., Medico, E.: Flame, a novel fuzzy clustering method for the analysis of dna microarray data. BMC Bioinf. 8, 1–17 (2007)
Acknowledgement
This work is supported in part by National Natural Science Foundation of China under Grant No. 61473045 and No. 41371425, and by the Program for Liaoning Innovative Research Team in University under Grant No. LT2013023.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hou, J., Sha, C., Cui, H., Chi, L. (2015). Cluster Merging Based on Dominant Sets. In: Feragen, A., Pelillo, M., Loog, M. (eds) Similarity-Based Pattern Recognition. SIMBAD 2015. Lecture Notes in Computer Science(), vol 9370. Springer, Cham. https://doi.org/10.1007/978-3-319-24261-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-24261-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24260-6
Online ISBN: 978-3-319-24261-3
eBook Packages: Computer ScienceComputer Science (R0)