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2016 | OriginalPaper | Buchkapitel

1. Discovering Cluster Dynamics Using Kernel Spectral Methods

verfasst von : Rocco Langone, Raghvendra Mall, Joos Vandewalle, Johan A. K. Suykens

Erschienen in: Complex Systems and Networks

Verlag: Springer Berlin Heidelberg

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Abstract

Networks represent patterns of interactions between components of complex systems present in nature, science, technology and society. Furthermore, graph theory allows to perform insightful analysis for different kinds of data by representing the instances as nodes of a weighted network, where the weights characterize similarity between the data points. In this chapter we describe a number of algorithms to perform cluster analysis, that is finding groups of similar items (called clusters or communities) and understand their evolution over time. These algorithms are designed in a kernel-based framework: the original data are mapped into an high dimensional feature space; linear models are designed in this space; complex nonlinear relationships between the data in the original input space can then be detected. Applications like fault detection in industrial machines, community detection of static and evolving networks, image segmentation, incremental time-series clustering and text clustering are considered.

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Nächstes Kapitel Community Detection in Bipartite Networks: Algorithms and Case studies
Fußnoten
1
If \(V = I\), problem (1.3) is equivalent to a kernel PCA formulation [22‐24].
 
3
A matlab implementation of the KSC algorithm is available at: http://​www.​esat.​kuleuven.​be/​stadius/​ADB/​alzate/​softwareKSClab.​php.
 
5
The software to generate the word clouds visualization is available at: http://​www.​wordle.​net/​.
 
6
A matlab implementation of the IKSC algorithm is available at: http://​www.​esat.​kuleuven.​be/​stadius/​ADB/​langone/​softwareIKSClab.​php.
 
Literatur
1.
Chung, F.R.K.: Spectral Graph Theory. American Mathematical Society, Providence (1997)MATH
2.
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRef
3.
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press, Cambridge (2002)
4.
5.
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the Nyström method. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 214–225 (2004)CrossRefMATH
6.
Williams, C.K.I., Seeger, M.: Using the Nyström method to speed up kernel machines. Advances in Neural Information Processing Systems, vol. 13. MIT Press, Cambridge (2001)
7.
Lin, F., Cohen, William, W.: Power iteration clustering. In: ICML, pp. 655–662 (2010)
8.
Ning, H., Xu, W., Chi, Y., Gong, Y., Huang, T.S.: Incremental spectral clustering by efficiently updating the eigen-system. Pattern Recognit. 43(1), 113–127 (2010)CrossRefMATH
9.
10.
Frederix, K., Van Marc, B.: Sparse spectral clustering method based on the incomplete Cholesky decomposition. J. Comput. Appl. Math. 237(1), 145–161 (2013)MathSciNetCrossRef
11.
Alzate, C., Suykens, J.A.K.: Multiway spectral clustering with out-of-sample extensions through weighted kernel PCA. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 335–347 (2010). (ESAT-SISTA, K.U. Leuven)CrossRef
12.
Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)CrossRefMATH
13.
Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, pp. 554–560. ACM, New York (2006)
14.
Chi, Y., Song, X., Zhou, D., Hino, K., Tseng, B.L.: Evolutionary spectral clustering by incorporating temporal smoothness. In: KDD’07, pp. 153–162 (2007)
15.
Xu, K.S., Kliger, M., Hero, III., Alfred, O.: Adaptive evolutionary clustering. Data Min. Knowl. Discov. 1–33 (2013)
16.
Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980), 876–878 (2010)MathSciNetCrossRef
17.
Chakraborty, S., Nagwani, N.K.: Analysis and study of incremental K-means clustering algorithm. High Perform. Archit. Grid Comput. 169, 338–341 (2011)CrossRef
18.
Rocco, L., Carlos, A., Suykens, J.A.K.: Kernel spectral clustering with memory effect. Phys. A: Stat. Mech. Appl. 392(10), 2588–2606 (2013)CrossRefMATH
19.
Rocco, L., Suykens, J.A.K.: Community detection using kernel spectral clustering with memory. J. Phys. Conf. Ser. 410(1), 012100 (2013)
20.
Langone, R., Mall, R., Suykens, J.A.K.: Clustering data over time using kernel spectral clustering with memory. In: SSCI (CIDM) (2014)
21.
Langone, R., Agudelo, O.M., De Moor, B., Suykens, J.A.K.: Incremental kernel spectral clustering for online learning of non-stationary data. Neurocomputing 139, 246–260 (2014)CrossRef
22.
Suykens, J.A.K., Van Gestel, T., Vandewalle, J., De Moor, B.: A support vector machine formulation to PCA analysis and its kernel version. IEEE Trans. Neural Netw. 14(2), 447–450 (2003)CrossRef
23.
Schölkopf, B., Smola, A.J., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319 (1998)CrossRef
24.
Mika, S., Schölkopf, B., Smola, A.J., Müller, K.R., Scholz, M., Rätsch, G.: Kernel PCA and de-noising in feature spaces. Advances in Neural Information Processing Systems, vol. 11. MIT Press, Cambridge (1999)
25.
Puzicha, J., Hofmann, T., Buhmann, J.: Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. Comput. Vis. Pattern Recognit. 267–272 (1997)
26.
Warren, L.T.: Clustering of time series data—a survey. Pattern Recognit. 38(11), 1857–1874 (2005)CrossRefMATH
27.
Meila, M., Shi, J.: A random walks view of spectral segmentation. In: Artificial Intelligence and Statistics AISTATS (2001)
28.
Meila, M., Shi, J.: Learning segmentation by random walks. Advances in Neural Information Processing Systems, vol. 13. MIT Press, Cambridge (2001)
29.
Delvenne, J.C., Yaliraki, S.N., Barahona, M.: Stability of graph communities across time scales. Proc. Natl. Acad. Sci. 107(29), 12755–12760 (2010)CrossRef
30.
Langone, R., Mall, R., Suykens, J.A.K.: Soft Kernel spectral clustering. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2013), pp. 1028–1035 (2013)
31.
Mall, R., Langone, R., Suykens, J.A.K.: Kernel spectral clustering for big data networks. Entropy (Special Issue on Big Data) 15(5), 1567–1586 (2013)MathSciNet
32.
Langone, R., Alzate, C., Suykens, J.A.K.: Modularity-based model selection for kernel spectral clustering. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2011), pp. 1849–1856 (2011)
33.
Langone, R., Alzate, C., Suykens, J.A.K.: Kernel spectral clustering for community detection in complex networks. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2012), pp. 2596–2603 (2012)
34.
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103(23), 8577–8582 (2006)CrossRef
35.
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)
36.
Alzate, C., Suykens, A.K.J.: Hierarchical kernel spectral clustering. Neural Netw. 35, 21–30 (2012)CrossRefMATH
37.
Alzate, C., Sinn, M.: Improved electricity load forecasting via kernel spectral clustering of smart meters. In: ICDM, pp. 943–948 (2013)
38.
Langone, R., Alzate, C., De Ketelaere, B., Suykens, J.A.K.: Kernel spectral clustering for predicting maintenance of industrial machines. In: IEEE Symposium Series on Computational Intelligence and data mining SSCI (CIDM) 2013, pp. 39–45 (2013)
39.
Langone, R., Alzate, C., De Ketelaere, B., Vlasselaer, J., Meert, W., Suykens, J.A.K.: LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines. Eng. Appl. Artif. Intell. 37, 268–278 (2015)CrossRef
40.
Mall, R., Langone, R., Suykens, J.A.K.: Multilevel hierarchical kernel spectral clustering for real-life large scale complex networks. PLoS ONE. Public Libr. Sci. 9(6), e99966 (2014)CrossRef
41.
McAuley, J., Jure, L.: Discovering social circles in ego networks. TKDD 8(1), 4 (2014)CrossRef
42.
Eagle, N., Pentland, A.S., Lazer, D.: Inferring social network structure using mobile phone data. PNAS 106(1), 15274–15278 (2009)CrossRef
43.
Derek, G., Padraig C.: Spectral co-clustering for dynamic bipartite graphs. In: Proceedings of the 1st Workshop on Dynamic Networks and Knowledge Discovery, Barcelona, Spain (2010)
44.
Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: theory and practice. IEEE Trans. Knowl. Data Eng. 15(3), 515–528 (2003)CrossRef
45.
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, VLDB ’03, pp. 81–92 (2003)
46.
Can, F.: Incremental clustering for dynamic information processing. ACM Trans. Inf. Syst. 11(2), 143–164 (1993)MathSciNetCrossRef
47.
Gupta, C., Grossman, R.L.: GenIc: a single-pass generalized incremental algorithm for clustering. In: SDM. SIAM (2004)
48.
Ning, H., Xu, W., Chi, Y., Gong, Y., Huang, T.S.: Incremental spectral clustering with application to monitoring of evolving blog communities. In: SDM (2007)
49.
Alzate, C., Suykens, J.A.K.: Out-of-Sample eigenvectors in kernel spectral clustering. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2011), pp. 2349–2356 (2011)
Metadaten
Titel
Discovering Cluster Dynamics Using Kernel Spectral Methods
verfasst von
Rocco Langone
Raghvendra Mall
Joos Vandewalle
Johan A. K. Suykens
Copyright-Jahr
2016
Verlag
Springer Berlin Heidelberg
Buch
Complex Systems and Networks

Print ISBN: 978-3-662-47823-3

Electronic ISBN: 978-3-662-47824-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-662-47824-0_1

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