CLUSTERING

APPROACHES/METHODS

  • I. Basic Problems
    1.1. Clustering
    1.2. Classification
    1.3. Sorting (multicriteria ranking)
    1.4. Hard clustering
    1.5. Fuzzy clustering
    1.6. Approximate clustering
    1.7. Dynamic clustering
    1.8. Clustering in large-scale data sets/networks
    1.9. Time-series clustering
  • II. Basic Approaches/Methods/Measurement issues
    2.1. Hierarchical clustering
    2.2. K-means clustering
    2.3. Spectral clustering
    2.4. Consensus clustering (voting-based consensus of cluster ensembles, consensus partitions)
    2.5. Cross-entropy method for clustering
    2.6. Clustering as assignment
    2.7. Support vector clustering
    2.8. Symbolic clustering
    2.9. Conceptual clustering
    2.10. Probabilistic methods in clustering
    2.11. Graph-based clustering (combinatorial approaches/models)
    2.12. Knowledge-based clustering, preference based clustering, interactive man-machine methods
    2.13. Neural networks based methods for clustering
    2.14. Region-based clustering
    2.15. Segmentation problems
    2.16. Clustering ensemble algorithms
    2.17. Measures of clustering quality
  • III. Combinatorial Aprpoaches/Methods
    3.1. Set partitioning
    3.2. Minimum spanning tree based clustering
    3.3. Clique/based clustering, clique-oriented aprpoaches (maximum clique problem, multi-partite clique proble, clique in multipartite grapgh, morphological clique)
    3.4.Correlation clustering
    3.5.Network communities based clustering (modularity, cliques, etc.)
    3.6. Cluster editing problems
    3.7. Dominant set based clustering
    3.8. Covering based clustering
  • IV. Other approaches to clustering
    4.1. Mulicriteria/multi-objective clustering
    4.2. Clustering of multi-type objects
    4.3. Multidimensional scaling
  • V. Main criteria for clustering solutuions
    5.1. Intra-cluster distance/proximity
    5.2. Size of cluster
    5.3. Number of clusters
    5.4. Inter-cluster distance/proximity
    5.5. Correlation clustering functional
    5.6. Quality of modularity
    5.7. Multicriteria quality
    5.8. Measurement methods for sorting solutions

    MAIN APPLICATIONS

    1. Data mining and knowledge discovery
    2. Chemistry, biology, gene expression, etc.
    3. Web systems, web services, information retireval
    4. Pattern recognition, image processing
    5. Medical/technical diagnosis
    6. Anomaly detection
    7. VLSI design
    8. Network design and management (communicaiton netwokrs, sensor networks)
    9. Routing in communication networks
    10. Economics/management (planning, marketing)
    11. Social network analysis
    12. Clustering in/of data streams
    13. Systems monitoring
    14. Education (evaluation, analysis, etc. )

    Researchers

  • 1. Prof. J.K. Jain (Michigan State Univ., College of Engineering, Dept. of CS and Engineering) (clustering, computer vision, pattern recognition, machine learning, image processing)
  • 2. Prof. Mark E.J. Newman (Michigan Univ., Dept. of Physics, Center of Study of Complex Systems) (network communities strcutures, network analysis)
  • 3. Prof. Donald C. Wunsch II, (Missouri Univ. nof Sceince and Technology, Dept. of Electrical and Computer Eng.) (clustering, neural networks, dynamic programming, evolutionary computation, fuzzy systems, etc.)
  • 4. Prof. Boris G. Mirkin (Birkbeck College, London, UK) (clustering, statistics, data mining, text analysis)
  • 5. Prof. Daniel Keim (Univ. of Konstanz, Germany) (clustering, data mining, multimedia databases, high-dimensional spaces, visualization, etc.)
  • 6. Pror. Hans-Peter Kriegel (Ludwig-Maximilians Universitat Munchen - LMU Munich, Germany) (data mining, clustering, correlation clustering, high dimensional data, ensemble methods)
  • 7. Prof. Tomas Seidl, RWTH Aachen Univ., Germany (Clustering, data mining, databases)
  • 8. Prof. Jorg Sander (Univ. of Alberta, Canada) (data mining, spatial and temporal data, clustering)
  • 9. Dr. Arthur Zimek (Ludwig-Maximilians Universitat Munchen - LMU Munich, Germany) (data mining, clustering, high dimensional data, ensemble methods)
  • 10. Prof. Nabil Becalel, National Research Council Canada (Information and Communications Technologies)
  • 11. Prof. Vladimir Batagelj, Univ. of Lubljana, Slovenia (clustering, social network analysis)
  • 12. Prof. Anuska Ferligoj, Univ. of Lubljana, Slovenia (clustering, social network analysis)
  • 13. Prof. Eva Tardos (Cornell Univ.) (general, approximation algorithms, networking, network design, routing, clustering, facility location, etc.)
  • 14. Prof. Jon Kleinberg (Cornell Univ.) (general, networking, etc.)
  • 15. Prof. Michael Trick (CMU, Tepper School of Business) (general, graph coloring, timetabling, combinatorial Benders approaches, etc.)
  • 16. Prof. James F. Peters (Univ. of Manitoba, Winnipeg, Canada) (topology of digital mages, visual patterns, pattern discovery, proximity spaces, near sets, etc.)
  • 17. Prof. Clara Rocha (Instituto Politecnico de Coimba, Portugal)
  • 18. Prof. Michel X. Goemans (MIT) (approximation algorithms, primal-dual algorithms, randomized algorithms, TSP, spanning trees, covering, general assignment problem, networking, scheduling, semidefinite programming, etc.)
  • 19. Prof. Michael O. Ball (Univ. of Maryland) (cliques, networking, transportation, logistics, etc.)
  • 20. Prof. Gilbert Laporte (HEC Montreal) (general, etc.)
  • 21. Prof. Matthias Erhgott (The Univ. of Auckland) (multicriteria combinatorial optimization, approximation algorithms, etc.)
  • 22. Prof. Xavier Gandibleux (The Univ. of Nantes) (multicriteria combinatorial optimization, global optimization, evolutionary multiobjective optimization, approximation algorithms, application in transportation, communication, etc.)
  • 23. Prof. Vangelis Th. Paschos (LAMSADE, Univ. Paris-Dauphine) (general, graph coloring, Steiner problem, TSP, approximation algorithms, on-line algorithms, reoptimization, etc.)
  • 24. Prof. Lior Rokach (Ben-Gurion Univ., Dept. of Information Systems Engineering, Israel) (machine learning, information retrieval, recommender systems, etc.)
  • 25. Prof. Nenad Mladenovic (Brunel Univ., UK) (AI, metaheuristics, location, clustering)
  • 26. Prof. Alexander V. Kelmanov (Sobolev Inst. of Mathematics, Russian Acad. of Sci.) (discrete olptimization, clusteting, pattern recognition, etc.)
  • 27. Prof. Shai Ben-David (Dept. of CS, Univ. of Waterloo) (foundations of clustering, classification tasks, machine learning, etc.)
  • 28. Prof. Margareta Ackerman (Dept. of CS, Florida State Univ.) (theoretical foundations of clustering, information retrieval, etc.)

    Research Groups, and Centers

  • Center for Discrete Mathematics & Theoretical Computer Science (DIMACS) (New Jersey, USA)
  • LANCS INITIATIVE Foundational Operational Research: Building Theory for Practice (UK Universities: Lancaster Univ., Nottingham Univ., Cardiff Univ., Southhampton Univ.)

    Journals

  • J. of Classification
  • Journal of Heuristcs
  • ACM Computing Surveys
  • ACM Trans. on KDD
  • SIAM Reviews
  • SIAM J. on Discrete Mathematics
  • SIAM J. on Optimization
  • SIAM J. on Computing
  • Applied Discrete Mathematics
  • INFOR
  • Networks
  • Naval Research Logistics
  • Operations Research Letters
  • Information Research Letters
  • Journal of Global Optimization
  • Journal of Algorithms
  • Omega
  • Discrete Optimization
  • TOP
  • Information Processing Letters
  • Theoretical Computer Science
  • Algorithmic Operations Research
  • International Transactions in Operational Research
  • Informatica (Lith.)
  • Data Mining and Knowledge Discovery
  • Pattern Recognition
  • Pattern Recognition Letters
  • Information Systems
  • Data Mining and Knowledge Discovery
  • Data and Knowledge Engineering
  • Fuzzy Sets and Systems
  • Int. Journal of Pattern Recognition and Artificial Intelligence
  • Journal of Machine Learning Research
  • Machine Learning
  • IEEE Trans. on KDE
  • IEEE Trans. on PAMI
  • IEEE Trans. on Fuzzy Systems
  • IEEE Trans. on SMC
  • IEEE Trans. on Mobile Computing
  • IEEE Trans. on Neural Networks
  • IEEE Trans. on Service Computing
  • Proc. of the IEEE
  • The Computer Journal
  • Computer Communications
  • Ad Hoc Networks
  • Int. Journal of Artificial Intelligence Tools
  • Annals of Operations Research
  • Computers and Industrial Engineering
  • Knowledge Information Systems
  • Journal of Combinatorial Optimization
  • Operations Research
  • Eur. Journal of Operational Research
  • Journal of the Operational Research Society
  • Computers and Operations Research
  • Algorithmica

    Bibliography

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    6. B.G. Mirkin, Mathematical Classification and Clustering. Kluwer, 1996.
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    8. M.E.J. Newman, Networks: an Introduction. Oxford Univ. Press, Oxford, 2010.
    9. J.V. de Oliveira, W. Pedrycz, Advances in Fuzzy Clustering and Its Applications. Wiley, 2007.
    10. W. Pedrycz, Knowledge-Based Clustering: From Data to Information Granules. Wiley, Hoboken, NJ, 2005.
    11. B. Roy, Multicriteria Methodology for Decision Aiding. Kluwer, Dordrecht, 1996.
    12. R.Y. Rubinstein, D.P. Kroese, The Cross Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer, 2004.

  • Collective Monographs
    1. F. Aleskerov, B. Goldengorin, P.M. Pardalos (Eds.) Clusters, Orders, and Trees: Methods and Applciations. Springer, 2014.
    2. P. Arabie, L.J. Hubert, G. De Soete (Eds.), Clustering and Classification. World Scientific, 1996.
    3. S.K. Halgamuge, L. Wang (eds), Classification and Clustering for Knowledge Discovery. Springer, 2005.
    4. D.S. Johnson, and M.A. Trick, (Eds.), Cliques, Coloring, and Satisfiability. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 26, Providence: AMS, 1996.
    5. J. Van Ryzin (Ed.), Classification and Clustering. Academic Press, New York, 1977.

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    13. M.Sh. Levin, Towards Combinatorial Clustering: Preliminary Research Survey. Electronic preprint. 102 p., May 28, 2015.
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    14. X. Liu, T. Murata, K. Wakita, Extending modularity by capturing the similarity attraction feature in the null model. Electronic preprint. 10 p., Feb. 12, 2013. http://arxiv.org/abs/1210.4007 [cs.SI]
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  • PhD Theses
    1. P. Bajcsy, Hierarchical segmentation and clustering using similarity analysis. PhD Dissertation, Dept. of CS, Univ. of Illinois at Urbana-Champaign, 1997.
    2. D. Kumlander, Some Practical Algorithms to Solve The Maximum Clique Problem. PhD Thesis, Tallin Univ. of Techn, 2005.
    3. R.R. Mettu, Approximation algorithms for NP-hard clustering problems. PhD Thesis, Dept. of CS, Univ. of Texas at Austin, Aug. 2002.
    4. Arthur Zimek, Correlation Clustering. PhD Thesis, Faculty of Mathematics, Informatics, and Statistics, Univ. of Munchen, 2008.
    5. Konstantin S. Solnushkin, Automated Design of Computer Clusters. PhD dissertation, Ludvig-Maximilians-Universitat, Faculty of Informatics, 2014.
    6. Margareta Ackerman, Towards Theoretical Foundations of Clustering. Dept. of CS, Univ. of Waterloo, 2012.

  • MS Theses
    1. R. Rotta, A multi-level algorithm for modularity clustering. MS thesis, Brandenburg Univ. of Technology, 2008.
    2. M. Landberg, Approximation Algorithms for Maximization Problems arising in Graph Partitioning. MS thesis, Weizmann Inst. of Science, 1998.



    This material was prepared within framework of Russian Science Foundation grant 14-50-00150 ``Digital technologies and their applications'' (project of Inst. for Information Transmission Problems).