• 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


    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. )


  • 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.)


  • 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
  • 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


  • Basic Books
    1. M.R. Anderberg, Cluster Analysis for Applications. Academic Press, New York, 1973.
    1. Th. H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms. The MIT Press, 2009.
    2. M.R. Garey, and D.S. Johnson, Computers and Intractability. The Guide to the Theory of NP-Completeness.
    San-Francisco: W.H. Freeman and Company, 1979.
    3. A. Gordon, Classification. 2nd ed., Chapman and Hall, London, 1999.
    4. A.K. Jain, R.C. Dubes, Algorithms for clustering data. Prentice Hall, Upper Saddle River, NJ, 1988.
    5. B.G. Mirkin, Group Choice. Winston, New York, 1979.
    6. B.G. Mirkin, Mathematical Classification and Clustering. Kluwer, 1996.
    7. B.G. Mirkin, Clustering for Data Mining: A Data Recovery Approach. Chapman & Hall/CRC, Boca Raton, FL, 2005.
    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.

  • Books
    1. A.V. Aho, J.E. Hopcroft, J.D. Ullman, The Design and Analysis of Computer Algorithms. Addison Welsey, Reading, MA, 1974.
    2. R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval. Addison-Wesley, 1999.
    3. P. Baldi, G. Hatfield, DNA Microarrays and Gene Expression. Cambridge Univ. Press, 2002.
    4. S. Bandyopadhyay, S. Saha, Unsupervised Classification: Similarity Measures, Classical and Metaheuristic Approaches. Springer, 2013.
    5. M.J.A. Berry, G. Linoff, Data Mining Techniques for Marketing, Sales and Customer Support. Wiley, 1996.
    6. M.W. Berry, M. Browne, Understanding Search Engines: Mathematical Modeling and Text Retrieval. SIAM, 1999.
    7. L. Billard, E. Diday, Symbolic Data Analysis. Wiley, 2007.
    8. I. Borg, P.J.F. Groenen, Modern Multidimensional Scaling: Theory and Applications. 2nd ed., Springer, New York, 2005.
    9. W.P. Cook, M. Kress, Ordinal Information and Preference Structures: Decision Models and Applications. Prentice-Hall, Englewood Cliffs, 1992.
    10. T.F. Cox, M.A.A. Cox, Multidimensional Scaling. CRC Press, 2000.
    11. M.L. Davidson, Multidimensional Scaling. Wiley, 1983.
    12. B. Duran, P. Odell, Cluster Analysis: A Survey. Springer, New York, 1974.
    13. B. Goldengorin, D. Krushinsky, P.M. Pardalos, Cell Formation in Industrial Engineering: Theory, Algorithms and Experiments. Springer, 2013.
    14. B.I. Goldengorin, Requiremments of Standards: Optimization Models and Algorithms. Hoogezand, The Netherlands: Operations Research Co., 1995.
    15. P.E. Green, F.J. Carmone Jr., S.M. Smith, Multidimensional Scaling: Concepts and Applications. Allyn and Bacon, Boston, 1989.
    16. J. Han, M. Kamber, Data Mining: Concepts and Techniques. 2nd ed., Morgan Kaufmann, 2005.
    17. D.J. Hand, H. Mannila, P. Smyth, Principles of Data Mining. The MIT Press, 2001.
    18. J.A. Hartigan, Clustering algorithms. Wiley, New York, 1975.
    19. F. Hoppner, F. Klawonn, R. Kruse, Fuzzy Cluster Analysis: Methods for Classification, Data Analysis, and Image Recognition. Wiley, New York, 1999.
    20. L.J. Hubert, Assignment Methods in Combinatorial Data Analysis. M. Dekker, New York, 1987.
    21. L. Kaufman, P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, 1990.
    22. V. Kumar, M. Steinbach, P.-N. Tan, Introduction to Data Mining. Addison-Wesley, 2005.
    23. M. Last, A. Kandel, Data mining in time series databases. World Sceintific, 2004.
    24. C.D. Manning, P. Raghavan, H. Schutze, Introduction to Information Retrieval. Cambridge Univ. Press, 2008.
    25. S. Miyamoto, Fuzzy Sets in Information Retrieval and Cluster Analysis. Kluwer, Dordrecht, 1990.
    26. F. Murtagh, Multidimensional Clustering Algorithms. Physica-Verlag, Vienna, 1985.
    27. S. Wassserman, K. Faust, Social Network Analysis: Methods and Applications. Cambridge Univ. Press, Cambridge, 1994.
    28. P. Willett, Similarity and Clustering in Chemical Information Systems. Research Studies Press, Letchworth, 1987.
    29. R. Xu, D. Wunsch, Clustering. Wiley-IEEE Press, 2009.
    30. F.W. Yuang, Multidimensional Scaling: History, Theory, and Applications. Psychology Press, 2013.
    31. J. Zupan, Clustering of Large Data Sets. Research Studies Press Ltd., Taunton, UK, 1982.

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    1. Matthias Ehrgott, A survey and annotated bibliography of multiobjective combinatorial optimization. OR Spectrum, 22(4), 425-460, 2000.
    2. V. Kumar, S. Minz, Feature selection: a literature review. Smart Comput. Review 4(3), 211--229, 2014.
    3. E.W.T. Ngai, L. Xiu, D.C.K. Chau, Application of data mining techniques in customer relationship management: A literature review and classification. ESwA 36(2), 2592--2602, 2009.

  • Surveys
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  • Some basic papers
    1. M. Ball and M. Magazine, The design and analysis of heuristics. Networks, 11(2), 215-219, 1981.
    2. N. Bansal, A. Blum, S. Chawla, Correlation clustering. Machine Learning 56(1-3), 89--113, 2004.
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    15. U. von Luxburg, A tutorial on spectral clustering. Electronic preprint, 32 p., Nov. 2007. [cs.DS]
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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).