Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

REFERENCES

• 1: Emile Aarts , Jan Korst, Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing, John Wiley & Sons, Inc., New York, NY, 1989
• 2: ACM, 1994. ACM CR Classifications. ACM Computing Surveys 35, 5-16.
• 3: AL-SULTAN, K.S. 1995. A tabu search approach to clustering problems. Pattern Recogn. 28, 1443-1451.
• 4: Khaled S. Al-Sultan , M. Maroof Khan, Computational experience on four algorithms for the hard clustering problem, Pattern Recognition Letters, v.17 n.3, p.295-308, March 6, 1996 [doi>10.1016/0167-8655(95)00122-0]
• 5: ALLEN, P. A. AND ALLEN, J. R. 1990. Basin Analysis: Principles and Applications. Blackwell Scientific Publications, Inc., Cambridge, MA.
• 6: ALTA VISTA, 1999. http://altavista.digital.com.
• 7: M. Amadasun , R. A. King, Low-level segmentation of multispectral images via agglomerative clustering of uniform neighbourhoods, Pattern Recognition, v.21 n.3, p.261-268, 1988 [doi>10.1016/0031-3203(88)90060-X]
• 8: ANDERBERG, M. R. 1973. Cluster Analysis for Applications. Academic Press, Inc., New York, NY.
• 9: J. Gary Augustson , Jack Minker, An Analysis of Some Graph Theoretical Cluster Techniques, Journal of the ACM (JACM), v.17 n.4, p.571-588, Oct. 1970 [doi>10.1145/321607.321608]
• 10: G. Phanendra Babu , M. Narasimha Murty, A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm, Pattern Recognition Letters, v.14 n.10, p.763-769, Oct. 1993 [doi>10.1016/0167-8655(93)90058-L]
• 11: BABU, G. P. AND MURTY, M.N. 1994. Clustering with evolution strategies. Pattern Recogn. 27, 321-329.
• 12: BABU, G. P., MURTY, M. N., AND KEERTHI, S. S. 2000. Stochastic connectionist approach for pattern clustering (To appear). IEEE Trans. Syst. Man Cybern.
• 13: BACKER, F. B. AND HUBERT, L.g. 1976. A graphtheoretic approach to goodness-of-fit in complete-link hierarchical clustering. J. Am. Stat. Assoc. 71,870-878.
• 14: E. Backer, Computer-assisted reasoning in cluster analysis, Prentice Hall International (UK) Ltd., Hertfordshire, UK, 1995
• 15: Ricardo A. Baeza-Yates, Introduction to data structures and algorithms related to information retrieval, Information retrieval: data structures and algorithms, Prentice-Hall, Inc., Upper Saddle River, NJ, 1992
• 16: Peter Bajcsy, Hierarchical segmentation and clustering using similarity analysis, University of Illinois at Urbana-Champaign, Champaign, IL, 1997
• 17: BALL, G. H. AND HALL, D.J. 1965. ISODATA, a novel method of data analysis and classification. Tech. Rep. Stanford University, Stanford, CA.
• 18: BENTLEY, J. L. AND FRIEDMAN, J.H. 1978. Fast algorithms for constructing minimal spanning trees in coordinate spaces. IEEE Trans. Comput. C-27, 6 (June), 97-105.
• 19: James C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA, 1981
• 20: BHUYAN, J. N., RAGHAVAN, V. V., AND VENKATESH, K.E. 1991. Genetic algorithm for clustering with an ordered representation. In Proceedings of the Fourth International Conference on Genetic Algorithms, 408-415.
• 21: BISWAS, G., WEINBERG, J., AND LI, C. 1995. A Conceptual Clustering Method for Knowledge Discovery in Databases. Editions Technip.
• 22: Victor L. Brailovsky, A probabilistic approach to clustering, Pattern Recognition Letters, v.12 n.4, p.193-198, April 1991 [doi>10.1016/0167-8655(91)90031-G]
• 23: BRODATZ, P. 1966. Textures: A Photographic Album for Artists and Designers. Dover Publications, Inc., Mineola, NY.
• 24: Fazli Can, Incremental clustering for dynamic information processing, ACM Transactions on Information Systems (TOIS), v.11 n.2, p.143-164, April 1993 [doi>10.1145/130226.134466]
• 25: CARPENTER, G. AND GROSSBERG, S. 1990. ART3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Networks 3, 129-152.
• 26: CHEKURI, C., GOLDWASSER, M. H., RAGHAVAN, P., AND UPFAL, E. 1997. Web search using automatic classification. In Proceedings of the Sixth International Conference on the World Wide Web (Santa Clara, CA, Apr.), http:// theory.stanford.edu/people/wass/publications/ Web Search/Web Search.html.
• 27: CHENG, C. H. 1995. A branch-and-bound clustering algorithm. IEEE Trans. Syst. Man Cybern. 25, 895-898.
• 28: CHENG, Y. AND FU, K.S. 1985. Conceptual clustering in knowledge organization. IEEE Trans. Pattern Anal. Mach. Intell. 7, 592-598.
• 29: Yizong Cheng, Mean Shift, Mode Seeking, and Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17 n.8, p.790-799, August 1995 [doi>10.1109/34.400568]
• 30: Yi-tzuu T. Chien, Interactive Pattern Recognition, Marcel Dekker, Inc., New York, NY, 1978
• 31: Sabyasachy Choudhury , M. Narasimha Murty, A divisive scheme for constructing minimal spanning trees in coordinate space, Pattern Recognition Letters, v.11 n.6, p.385-389, Jun. 1990 [doi>10.1016/0167-8655(90)90108-E]
• 32: 1996. Special issue on data mining. Commun. ACM 39, 11.
• 33: COLEMAN, G. B. AND ANDREWS, H. C. 1979. Image segmentation by clustering. Proc. IEEE 67, 5, 773-785.
• 34: Scott D. Connell , Anil K. Jain, Learning Prototypes for On-Line Handwritten Digits, Proceedings of the 14th International Conference on Pattern Recognition-Volume 1, p.182, August 16-20, 1998
• 35: Stephen E. Cross, Intelligent systems span generations, IEEE Expert: Intelligent Systems and Their Applications, v.11 n.5, p.2-3, October 1996
• 36: DALE, M. B. 1985. On the comparison of conceptual clustering and numerical taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 7, 241-244.
• 37: DAVE, R. N. 1992. Generalized fuzzy C-shells clustering and detection of circular and elliptic boundaries. Pattern Recogn. 25, 713-722.
• 38: DAVIS, T., Ed. 1991. The Handbook of Genetic Algorithms. Van Nostrand Reinhold Co., New York, NY.
• 39: DAY, W. H.E. 1992. Complexity theory: An introduction for practitioners of classification. In Clustering and Classification, P. Arabie and L. Hubert, Eds. World Scientific Publishing Co., Inc., River Edge, NJ.
• 40: DEMPSTER, A. P., LAIRD, N. M., AND RUB IN, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. B. 39, 1, 1-38.
• 41: DIDAY, E. 1973. The dynamic cluster method in non-hierarchical clustering. J. Comput. Inf. Sci. 2, 61-88.
• 42: DIDAY, E. AND SIMON, J. C. 1976. Clustering analysis. In Digital Pattern Recognition, K. S. Fu, Ed. Springer-Verlag, Secaucus, NJ, 47-94.
• 43: DIDAY, E. 1988. The symbolic approach in clustering. In Classification and Related Methods, H. H. Bock, Ed. North-Holland Publishing Co., Amsterdam, The Netherlands.
• 44: C. Dorai , A. K. Jain, Shape spectra based view grouping for free-form objects, Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3, p.3340, October 23-26, 1995
• 45: DUBES, R. C. AND JAIN, A. K. 1976. Clustering techniques: The user's dilemma. Pattern Recogn. 8, 247-260.
• 46: DUBES, R. C. AND JAIN, A. K. 1980. Clustering methodology in exploratory data analysis. In Advances in Computers, M. C. Yovits,, Ed. Academic Press, Inc., New York, NY, 113- 125.
• 47: Richard C. Dubes, How many clusters are best?—an experiment, Pattern Recognition, v.20 n.6, p.645-663, November 1, 1987 [doi>10.1016/0031-3203(87)90034-3]
• 48: Richard C. Dubes, Cluster analysis and related issues, Handbook of pattern recognition & computer vision, World Scientific Publishing Co., Inc., River Edge, NJ, 1993
• 49: DUBUISSON, M. P. AND JAIN, A.K. 1994. A modified Hausdorff distance for object matching. In Proceedings of the International Conference on Pattern Recognition (ICPR '94), 566-568.
• 50: Richard O. Duda , Peter E. Hart , David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
• 51: DUNN, S., JANOS, L., AND ROSENFELD, A. 1983. Bimean clustering. Pattern Recogn. Lett. 1, 169-173.
• 52: DURAN, B. S. AND ODELL, P. L. 1974. Cluster Analysis: A Survey. Springer-Verlag, New York, NY.
• 53: William F. Eddy , Audris Mockus , Shingo Oue, Approximate single linkage cluster analysis of large data sets in high-dimensional spaces, Computational Statistics & Data Analysis, v.23 n.1, p.29-43, Nov. 15, 1996 [doi>10.1016/S0167-9473(96)00019-9]
• 54: Oren Etzioni, The World-Wide Web: quagmire or gold mine?, Communications of the ACM, v.39 n.11, p.65-68, Nov. 1996 [doi>10.1145/240455.240473]
• 55: EVERITT, B.S. 1993. Cluster Analysis. Edward Arnold, Ltd., London, UK.
• 56: FABER, V. 1994. Clustering and the continuous k-means algorithm. Los Alamos Science 22, 138-144.
• 57: FABER, V., HOCHBERG, J. C., KELLY, P. M., THOMAS, T. R., AND WHITE, J.M. 1994. Concept extraction: A data-mining technique. Los Alamos Science 22, 122-149.
• 58: Usama M. Fayyad, Data Mining and Knowledge Discovery: Making Sense Out of Data, IEEE Expert: Intelligent Systems and Their Applications, v.11 n.5, p.20-25, October 1996 [doi>10.1109/64.539013]
• 59: Douglas Fisher , Pat Langley, Conceptual clustering and its relation to numerical taxonomy, Artificial intelligence and statistics, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1986
• 60: Douglas H. Fisher, Knowledge Acquisition Via Incremental Conceptual Clustering, Machine Learning, v.2 n.2, p.139-172, September 1987 [doi>10.1023/A:1022852608280]
• 61: Doug Fisher , Ling Xu , James R. Carnes , Yoram Reich , Steven J. Fenves , Jason Chen , Richard Shiavi , Gautam Biswas , Jerry Weinberg, Applying AI Clustering to Engineering Tasks, IEEE Expert: Intelligent Systems and Their Applications, v.8 n.6, p.51-60, December 1993 [doi>10.1109/64.248353]
• 62: FISHER, L. AND VAN NESS, J. W. 1971. Admissible clustering procedures. Biometrika 58, 91-104.
• 63: Patrick J. Flynn , Anil K. Jain, BONSAI: 3D Object Recognition Using Constrained Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.13 n.10, p.1066-1075, October 1991 [doi>10.1109/34.99239]
• 64: FOGEL, D. B. AND SIMPSON, P.K. 1993. Evolving fuzzy clusters. In Proceedings of the International Conference on Neural Networks (San Francisco, CA), 1829-1834.
• 65: FOGEL, D. B. AND FOGEL, L. J., Eds. 1994. Special issue on evolutionary computation. IEEE Trans. Neural Netw. (Jan.).
• 66: FOGEL, L. J., OWENS, A. J., AND WALSH, M. J. 1965. Artificial Intelligence Through Simulated Evolution. John Wiley and Sons, Inc., New York, NY.
• 67: William B. Frakes , Ricardo Baeza-Yates, Information retrieval: data structures and algorithms, Prentice-Hall, Inc., Upper Saddle River, NJ, 1992
• 68: FRED, A. L. N. AND LEITAO, J. M. N. 1996. A minimum code length technique for clustering of syntactic patterns. In Proceedings of the International Conference on Pattern Recognition (Vienna, Austria), 680-684.
• 69: Ana L. N. Fred, Clustering of sequences using minimum grammar compexity criterion, Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences, p.107-116, September 25-27, 1996
• 70: Fu, K. S. AND LU, S.Y. 1977. A clustering procedure for syntactic patterns. IEEE Trans. Syst. Man Cybern. 7, 734-742.
• 71: Fu, K. S. AND MUI, J. K. 1981. A survey on image segmentation. Pattern Recogn. 13, 3-16.
• 72: Keinosuke Fukunaga, Introduction to statistical pattern recognition (2nd ed.), Academic Press Professional, Inc., San Diego, CA, 1990
• 73: Fred Glover, Future paths for integer programming and links to artificial intelligence, Computers and Operations Research, v.13 n.5, p.533-549, May 1986 [doi>10.1016/0305-0548(86)90048-1]
• 74: David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
• 75: GORDON, A. D. AND HENDERSON, J. T. 1977. Algorithm for Euclidean sum of squares. Biometrics 33, 355-362.
• 76: S. Kumar, Semantic Clustering of Index Terms, Journal of the ACM (JACM), v.15 n.4, p.493-513, Oct. 1968 [doi>10.1145/321479.321480]
• 77: GOWDA, K. C. 1984. A feature reduction and unsupervised classification algorithm for multispectral data. Pattern Recogn. 17, 6, 667- 676.
• 78: GOWDA, K. C. AND KRISHNA, G. 1977. Agglomerative clustering using the concept of mutual nearest neighborhood. Pattern Recogn. 10, 105-112.
• 79: GOWDA, K. C. AND DIDAY, E. 1992. Symbolic clustering using a new dissimilarity meG- sure. IEEE Trans. Syst. Man Cybern. 22, 368-378.
• 80: GOWER, J. C. AND ROSS, G. J.S. 1969. Minimum spanning rees and single-linkage cluster analysis. Appl. Stat. 18, 54-64.
• 81: J Grefenstette, Optimization of control parameters for genetic algorithms, IEEE Transactions on Systems, Man and Cybernetics, v.16 n.1, p.122-128, Jan./Feb. 1986 [doi>10.1109/TSMC.1986.289288]
• 82: HARALICK, R. M. AND KELLY, G. L. 1969. Pattern recognition with measurement space and spatial clustering for multiple images. Proc. IEEE 57, 4, 654-665.
• 83: John A. Hartigan, Clustering Algorithms, John Wiley & Sons, Inc., New York, NY, 1975
• 84: Sara Reese Hedberg, Searching for the mother lode: tales of the first data miners, IEEE Expert: Intelligent Systems and Their Applications, v.11 n.5, p.4-7, October 1996 [doi>10.1109/64.539006]
• 85: John Hertz , Anders Krogh , Richard G. Palmer, Introduction to the theory of neural computation, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1991
• 86: R. Hoffman , A. K. Jain, Segmentation and classification of range images, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.9 n.5, p.608-620, Sept. 1987 [doi>10.1109/TPAMI.1987.4767955]
• 87: Thomas Hofmann , Joachim M. Buhmann, Pairwise Data Clustering by Deterministic Annealing, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.1, p.1-14, January 1997 [doi>10.1109/34.566806]
• 88: Thomas Hofmann , Jan Puzicha , Joachim M. Buchmann, Unsupervised Texture Segmentation in a Deterministic Annealing Framework, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.20 n.8, p.803-818, August 1998 [doi>10.1109/34.709593]
• 89: John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
• 90: Adam Hoover , Gillian Jean-Baptiste , Xiaoyi Jiang , Patrick J. Flynn , Horst Bunke , Dmitry B. Goldgof , Kevin Bowyer , David W. Eggert , Andrew Fitzgibbon , Robert B. Fisher, An Experimental Comparison of Range Image Segmentation Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.18 n.7, p.673-689, July 1996 [doi>10.1109/34.506791]
• 91: D. P. Huttenlocher , G. A. Klanderman , W. A. Rucklidge, Comparing Images Using the Hausdorff Distance, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.15 n.9, p.850-863, September 1993 [doi>10.1109/34.232073]
• 92: ICHINO, M. AND YAGUCHI, H. 1994. Generalized Minkowski metrics for mixed feature-type data analysis. IEEE Trans. Syst. Man Cybern. 24, 698-708.
• 93: 1991. Proceedings of the International Joint Conference on Neural Networks. (IJCNN'91).
• 94: 1992. Proceedings of the International Joint Conference on Neural Networks.
• 95: M. A. Ismail , M. S. Kamel, Multidimensional data clustering utilizing hybrid search strategies, Pattern Recognition, v.22 n.1, p.75-89, January 1989 [doi>10.1016/0031-3203(89)90040-X]
• 96: Anil K. Jain , Richard C. Dubes, Algorithms for clustering data, Prentice-Hall, Inc., Upper Saddle River, NJ, 1988
• 97: Anil K. Jain , Farshid Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, v.24 n.12, p.1167-1186, Dec. 1991 [doi>10.1016/0031-3203(91)90143-S]
• 98: Anil K. Jain , Sushil Bhattacharjee, Text segmentation using Gabor filters for automatic document processing, Machine Vision and Applications, v.5 n.3, p.169-184, Summer 1992 [doi>10.1007/BF02626996]
• 99: Anil K. Jain , Patrick J. Flynn, Three-Dimensional Object Recognition Systems, Elsevier Science Inc., New York, NY, 1993
• 100: JAIN, A. K. AND MAO, J. 1994. Neural networks and pattern recognition. In Computational Intelligence: Imitating Life, J. M. Zurada, R. J. Marks, and C. J. Robinson, Eds. 194- 212.
• 101: JAIN, A. K. AND FLYNN, P.J. 1996. Image segmentation using clustering. In Advances in Image Understanding: A Festschrift for Azriel Rosenfeld, N. Ahuja and K. Bowyer, Eds, IEEE Press, Piscataway, NJ, 65-83.
• 102: Anil K. Jain , Jianchang Mao , K. M. Mohiuddin, Artificial Neural Networks: A Tutorial, Computer, v.29 n.3, p.31-44, March 1996 [doi>10.1109/2.485891]
• 103: JAIN, A. K., RATHA, N. K., AND LAKSHMANAN, S. 1997. Object detection using Gabor filters. Pattern Recogn. 30, 2, 295-309.
• 104: Naresh C. Jain , Abhaya Indrayan , Lajpat R. Goel, Monte Carlo comparison of six hierarchical clustering methods on random data, Pattern Recognition, v.19 n.1, p.95-99, Jan./Feb. 1986 [doi>10.1016/0031-3203(86)90038-5]
• 105: Ramesh Jain , Rangachar Kasturi , Brian G. Schunck, Machine vision, McGraw-Hill, Inc., New York, NY, 1995
• 106: JARVIS, R. A. AND PATRICK, E. A. 1973. Clustering using a similarity method based on shared near neighbors. IEEE Trans. Comput. C-22, 8 (Aug.), 1025-1034.
• 107: Jean-Michel Jolion , Peter Meer , Samira Bataouche, Robust Clustering with Applications in Computer Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.13 n.8, p.791-802, August 1991 [doi>10.1109/34.85669]
• 108: JONES, D. AND BELTRAMO, M.A. 1991. Solving partitioning problems with genetic algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms, 442-449.
• 109: Large-Scale Parallel Data Clustering, Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472, p.488, August 25-29, 1996
• 110: KING, B. 1967. Step-wise clustering procedures. J. Am. Stat. Assoc. 69, 86-101.
• 111: KIRKPATRICK, S., GELATT, C. D., JR., AND VECCHI, M.P. 1983. Optimization by simulated annealing. Science 220, 4598 (May), 671-680.
• 112: KLEIN, R. W. AND DUBES, R. C. 1989. Experiments in projection and clustering by simulated annealing. Pattern Recogn. 22, 213-220.
• 113: Donald E. Knuth, The Art of Computer Programming Volumes 1-3 Boxed Set, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1998
• 114: KOONTZ, W. L. G., FUKUNAGA, K., AND NARENDRA, P.M. 1975. A branch and bound clustering algorithm. IEEE Trans. Comput. 23, 908- 914.
• 115: T. Kohonen, Self-organization and associative memory: 3rd edition, Springer-Verlag New York, Inc., New York, NY, 1989
• 116: KRAAIJVELD, M., MAO, J., AND JAIN, A. K. 1995. A non-linear projection method based on Kohonen's topology preserving maps. IEEE Trans. Neural Netw. 6, 548-559.
• 117: KRISHNAPURAM, R., FRIGUI, H., AND NASRAOUI, O. 1995. Fuzzy and probabilistic shell clustering algorithms and their application to boundary detection and surface approximation. IEEE Trans. Fuzzy Systems 3, 29-60.
• 118: Takio Kurita, An efficient agglomerative clustering algorithm using a heap, Pattern Recognition, v.24 n.3, p.205-209, 1991 [doi>10.1016/0031-3203(91)90062-A]
• 119: LIBRARY OF CONGRESS, 1990. LC classification outline. Library of Congress, Washington, DC.
• 120: Michael Lebowitz, Experiments with Incremental Concept Formation: UNIMEM, Machine Learning, v.2 n.2, p.103-138, September 1987 [doi>10.1023/A:1022800624210]
• 121: Hing-Yan Lee , Hwee-Leng Ong, Visualization Support for Data Mining, IEEE Expert: Intelligent Systems and Their Applications, v.11 n.5, p.69-75, October 1996 [doi>10.1109/64.539019]
• 122: LEE, R. C. T., SLAGLE, J. R., AND MONG, C. T. 1978. Towards automatic auditing of records. IEEE Trans. Softw. Eng. 4, 441- 448.
• 123: LEE, R. C. T. 1981. Cluster analysis and its applications. In Advances in Information Systems Science, J. T. Tou, Ed. Plenum Press, New York, NY.
• 124: LI, C. AND BISWAS, G. 1995. Knowledge-based scientific discovery in geological databases. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (Montreal, Canada, Aug. 20-21), 204 -209.
• 125: Lu, S. Y. AND FU, K. S. 1978. A sentence-tosentence clustering procedure for pattern analysis. IEEE Trans. Syst. Man Cybern. 8, 381-389.
• 126: LUNDERVOLD, A., FENSTAD, A. M., ERSLAND, L., AND TAXT, T. 1996. Brain tissue volumes from multispectral 3D MRI: A comparative study of four classifiers. In Proceedings of the Conference of the Society on Magnetic Resonance,
• 127: Yoelle S. Maarek , Israel Z. Ben Shaul, Automatically organizing bookmarks per contents, Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems, p.1321-1333, May 1996, Paris, France
• 128: MCQUEEN, J. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 281-297.
• 129: Jianchang Mao , Anil K. Jain, Texture classification and segmentation using multiresolution simultaneous autoregressive models, Pattern Recognition, v.25 n.2, p.173-188, Feb. 1992 [doi>10.1016/0031-3203(92)90099-5]
• 130: MAO, J. AND JAIN, A.K. 1995. Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans. Neural Netw. 6, 296-317.
• 131: MAO, J. AND JAIN, A.K. 1996. A self-organizing network for hyperellipsoidal clustering (HEC). IEEE Trans. Neural Netw. 7, 16-29.
• 132: Arthur J. Nevins, A Branch and Bound Incremental Conceptual Clusterer, Machine Learning, v.18 n.1, p.5-22, Jan. 1995 [doi>10.1023/A:1022881422818]
• 133: MICHALSKI, R., STEPP, R. E., AND DIDAY, E. 1981. A recent advance in data analysis: Clustering objects into classes characterized by conjunctive concepts. In Progress in Pattern Recognition, Vol. 1, L. Kanal and A. Rosenfeld, Eds. North-Holland Publishing Co., Amsterdam, The Netherlands.
• 134: MICHALSKI, R., STEPP, R. E., AND DIDAY, E. 1983. Automated construction of classifications: conceptual clustering versus numerical taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5, 5 (Sept.), 396-409.
• 135: MISHRA, S. K. AND RAGHAVAN, V. V. 1994. An empirical study of the performance of heuristic methods for clustering. In Pattern Recognition in Practice, E. S. Gelsema and L. N. Kanal, Eds. 425-436.
• 136: Thomas M. Mitchell, Machine Learning, McGraw-Hill Higher Education, 1997
• 137: MOHIUDDIN, K. M. AND MAO, g. 1994. A comparative study of different classifiers for handprinted character recognition. In Pattern Recognition in Practice, E. S. Gelsema and L. N. Kanal, Eds. 437-448.
• 138: MOOR, B.K. 1988. ART 1 and Pattern Clustering. In 1988 Connectionist Summer School, Morgan Kaufmann, San Mateo, CA, 174-185.
• 139: MURTAGH, F. 1984. A survey of recent advances in hierarchical clustering algorithms which use cluster centers. Comput. J. 26, 354-359.
• 140: MURTY, M. N. AND KRISHNA, G. 1980. A computationally efficient technique for data clustering. Pattern Recogn. 12, 153-158.
• 141: MURTY, M. N. AND JAIN, A.K. 1995. Knowledgebased clustering scheme for collection management and retrieval of library books. Pattern Recogn. 28, 949-964.
• 142: NAGY, G. 1968. State of the art in pattern recognition. Proc. IEEE 56, 836-862.
• 143: Raymond T. Ng , Jiawei Han, Efficient and Effective Clustering Methods for Spatial Data Mining, Proceedings of the 20th International Conference on Very Large Data Bases, p.144-155, September 12-15, 1994
• 144: Hong Hai Nguyen , Paul Cohen, Gibbs random fields, fuzzy clustering, and the unsupervised segmentation of textured images, CVGIP: Graphical Models and Image Processing, v.55 n.1, p.1-19, Jan. 1993 [doi>10.1006/cgip.1993.1001]
• 145: Karen L. Oehler , Robert M. Gray, Combining Image Compression and Classification Using Vector Quantization, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17 n.5, p.461-473, May 1995 [doi>10.1109/34.391396]
• 146: OJA, E. 1982. A simplified neuron model as a principal component analyzer. Bull. Math. Bio. 15, 267-273.
• 147: OZAWA, K. 1985. A stratificational overlapping cluster scheme. Pattern Recogn. 18, 279-286.
• 148: OPEN TEXT, 1999. http://index.opentext.net.
• 149: KAMGAR-PARSI, B., GUALTIERI, J. A., DEVANEY, J. A., AND KAMGAR-PARSI, K. 1990. Clustering with neural networks. Biol. Cybern. 63, 201-208.
• 150: LYCOS, 1999. http://www.lycos.com.
• 151: PAL, N. R., BEZDEK, J. C., AND TSAO, E. C.-K. 1993. Generalized clustering networks and Kohonen's self-organizing scheme. IEEE Trans. Neural Netw. 4, 549-557.
• 152: QUINLAN, J. R. 1990. Decision trees and decision making. IEEE Trans. Syst. Man Cybern. 20, 339-346.
• 153: Vijay V. Raghavan , Kim Birchard, A clustering strategy based on a formalism of the reproductive process in natural systems, Proceedings of the 2nd annual international ACM SIGIR conference on Information storage and retrieval: information implications into the eighties, p.10-22, September 27-28, 1979, Dallas, Texas
• 154: RAGHAVAN, V. V. AND YU, C.T. 1981. A comparison of the stability characteristics of some graph theoretic clustering methods. IEEE Trans. Pattern Anal. Mach. Intell. 3, 393-402.
• 155: Edie Rasmussen, Clustering algorithms, Information retrieval: data structures and algorithms, Prentice-Hall, Inc., Upper Saddle River, NJ, 1992
• 156: Elaine Rich, Artificial intelligence, McGraw-Hill, Inc., New York, NY, 1983
• 157: B. D. Ripley, Statistical Inference for Spatial Processes, Cambridge University Press, New York, NY, 1989
• 158: K. Rose , E. Gurewitz , G. C. Fox, Constrained Clustering as an Optimization Method, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.15 n.8, p.785-794, August 1993 [doi>10.1109/34.236251]
• 159: Azriel Rosenfeld , Avinash C. Kak, Digital Picture Processing, Academic Press, Inc., Orlando, FL, 1982
• 160: ROSENFELD, A., SCHNEIDER, V. B., AND HUANG, M. K. 1969. An application of cluster detection to text and picture processing. IEEE Trans. Inf. Theor. 15, 6, 672-681.
• 161: Ross, G. J. S. 1968. Classification techniques for large sets of data. In Numerical Taxonomy, A. J. Cole, Ed. Academic Press, Inc., New York, NY.
• 162: RuSPINI, E.H. 1969. A new approach to clustering. Inf. Control 15, 22-32.
• 163: SALTON, G. 1991. Developments in automatic text retrieval. Science 253, 974-980.
• 164: Ashok Samal , Prasana A. Iyengar, Automatic recognition and analysis of human faces and facial expressions: a survey, Pattern Recognition, v.25 n.1, p.65-77, Jan. 1992 [doi>10.1016/0031-3203(92)90007-6]
• 165: SAMMON, J. W. JR. 1969. A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 18, 401-409.
• 166: Rajeev Sangal, Programming Paradigms in LISP: Tools, Techniques, and Principles, McGraw-Hill, Inc., New York, NY, 1990
• 167: SCHACHTER, B. J., DAVIS, L. S., AND ROSENFELD, A. 1979. Some experiments in image segmentation by clustering of local feature values. Pattern Recogn. 11, 19-28.
• 168: Hans-Paul Schwefel, Numerical Optimization of Computer Models, John Wiley & Sons, Inc., New York, NY, 1981
• 169: SELIM, S. Z. AND ISMAIL, M.A. 1984. K-meanstype algorithms: A generalized convergence theorem and characterization of local optimality. IEEE Trans. Pattern Anal. Mach. Intell. 6, 81-87.
• 170: Shokri Z. Selim , K. Alsultan, A simulated annealing algorithm for the clustering problem, Pattern Recognition, v.24 n.10, p.1003-1008, 1991 [doi>10.1016/0031-3203(91)90097-O]
• 171: SEN, A. AND SRIVASTAVA, M. 1990. Regression Analysis. Springer-Verlag, New York, NY.
• 172: I. K. Sathi , A. K. Jain, Artificial Neural Networks and Statistical Pattern Recognition, Elsevier Science Inc., New York, NY, 1991
• 173: B. Shekar , Narasimha M. Murty , G. Krishna, A knowledge-based clustering scheme, Pattern Recognition Letters, v.5 n.4, p.253-259, April 1, 1987 [doi>10.1016/0167-8655(87)90054-7]
• 174: Judith F. Silverman , David B. Cooper, Bayesian Clustering for Unsupervised Estimation of Surface and Texture Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.10 n.4, p.482-495, July 1988 [doi>10.1109/34.3912]
• 175: Evangelos Simoudis, Reality Check for Data Mining, IEEE Expert: Intelligent Systems and Their Applications, v.11 n.5, p.26-33, October 1996 [doi>10.1109/64.539014]
• 176: SLAGLE, J. R., CHANG, C. L., AND HELLER, S. R. 1975. A clustering and data-reorganizing algorithm. IEEE Trans. Syst. Man Cybern. 5, 125-128.
• 177: SNEATH, P. H. A. AND SOKAL, R. R. 1973. Numerical Taxonomy. Freeman, London, UK.
• 178: SPATH, H. 1980. Cluster Analysis Algorithms for Data Reduction and Classification. Ellis Horwood, Upper Saddle River, NJ.
• 179: SOLBERG, A., TAXT, T., AND JAIN, A. 1996. A Markov random field model for classification of multisource satellite imagery. IEEE Trans. Geoscience and Remote Sensing 34, 1, 100-113.
• 180: A. Srivastava , M. N Murty, A comparison between conceptual clustering and conventional clustering, Pattern Recognition, v.23 n.9, p.975-981, 1990 [doi>10.1016/0031-3203(90)90106-U]
• 181: STAHL, H. 1986. Cluster analysis of large data sets. In Classification as a Tool of Research, W. Gaul and M. Schader, Eds. Elsevier North-Holland, Inc., New York, NY, 423-430.
• 182: Robert E. Stepp , Ryszard S. Michalski, Conceptual clustering of structured objects: a goal-oriented approach, Artificial Intelligence, v.28 n.1, p.43-69, Feb. 1986 [doi>10.1016/0004-3702(86)90030-5]
• 183: SUTTON, M., STARK, L., AND BOWYER, K. 1993. Function-based generic recognition for multiple object categories. In Three-Dimensional Object Recognition Systems, A. Jain and P. J. Flynn, Eds. Elsevier Science Inc., New York, NY.
• 184: SYMON, M. J. 1977. Clustering criterion and multi-variate normal mixture. Biometrics 77, 35-43.
• 185: TANAKA, E. 1995. Theoretical aspects of syntactic pattern recognition. Pattern Recogn. 28, 1053-1061.
• 186: TAXT, T. AND LUNDERVOLD, A. 1994. Multispectral analysis of the brain using magnetic resonance imaging. IEEE Trans. Medical Imaging 13, 3, 470-481.
• 187: TITTERINGTON, D. M., SMITH, A. F. M., AND MAKOV, U.E. 1985. Statistical Analysis of Finite Mixture Distributions. John Wiley and Sons, Inc., New York, NY.
• 188: TOUSSAINT, G. T. 1980. The relative neighborhood graph of a finite planar set. Pattern Recogn. 12, 261-268.
• 189: Øivind Due Trier , Torfinn Taxt, Evaluation of Binarization Methods for Document Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17 n.3, p.312-315, March 1995 [doi>10.1109/34.368197]
• 190: Toshio Uchiyama , Michael A. Arbib, Color Image Segmentation using Competitive Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.16 n.12, p.1197-1206, December 1994 [doi>10.1109/34.387488]
• 191: URQUHART, R.B. 1982. Graph theoretical clustering based on limited neighborhood sets. Pattern Recogn. 15, 173-187.
• 192: N. B. Venkateswarlu , P. S. V. S. K. Raju, Fast ISODATA clustering algorithms, Pattern Recognition, v.25 n.3, p.335-342, March 1992 [doi>10.1016/0031-3203(92)90114-X]
• 193: VINOD, V. V., CHAUDHURY, S., MUKHERJEE, J., AND GHOSE, S. 1994. A connectionist approach for clustering with applications in image analysis. IEEE Trans. Syst. Man Cybern. 24, 365-384.
• 194: WAH, B. W., Ed. 1996. Special section on mining of databases. IEEE Trans. Knowl. Data Eng. (Dec.).
• 195: WARD, J. H. JR. 1963. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236-244.
• 196: Satosi Watanabe, Pattern recognition: human and mechanical, John Wiley & Sons, Inc., New York, NY, 1985
• 197: WESZKA, J. 1978. A survey of threshold selection techniques. Pattern Recogn. 7, 259-265.
• 198: Darrell Whitley , Timothy Starkweather , D'Ann Fuquay, Scheduling problems and traveling salesman: the genetic edge recombination, Proceedings of the third international conference on Genetic algorithms, p.133-140, December 1989, George Mason University, United States
• 199: WILSON, D. R. AND MARTINEZ, T. R. 1997. Improved heterogeneous distance functions. J. Artif Intell. Res. 6, 1-34.
• 200: Z. Wu , R. Leahy, An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.15 n.11, p.1101-1113, November 1993 [doi>10.1109/34.244673]
• 201: Marilyn R. Wulfekuhler , William F. Punch, Finding salient features for personal Web page categories, Selected papers from the sixth international conference on World Wide Web, p.1147-1156, September 1997, Santa Clara, California, United States
• 202: ZADEH, L.A. 1965. Fuzzy sets. Inf. Control 8, 338 -353.
• 203: ZAHN, C. T. 1971. Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. C-20 (Apr.), 68-86.
• 204: ZHANG, K. 1995. Algorithms for the constrained editing distance between ordered labeled trees and related problems. Pattern Recogn. 28, 463-474.
• 205: Jianping Zhang , Ryszard S. Michalski, An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts, Machine Learning, v.21 n.3, p.235-267, Dec. 1995 [doi>10.1023/A:1022608301842]
• 206: Tian Zhang , Raghu Ramakrishnan , Miron Livny, BIRCH: an efficient data clustering method for very large databases, ACM SIGMOD Record, v.25 n.2, p.103-114, June 1996
• 207: ZUPAN, J. 1982. Clustering of Large Data Sets. Research Studies Press Ltd., Taunton, UK.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Computer vision

General Terms:
Algorithms

Keywords:
cluster analysis, clustering applications, exploratory data analysis, incremental clustering, similarity indices, unsupervised learning