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Information Processing and Management: an International Journal
Algorithms for clustering data
Algorithms for clustering data
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Handbook of pattern recognition & computer vision
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Advances in knowledge discovery and data mining
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Parallel programming: techniques and applications using networked workstations and parallel computers
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STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Clustering Algorithms
Self-Organizing Maps
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ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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ICDT '99 Proceedings of the 7th International Conference on Database Theory
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Parallel Clustering Algorithms on a Reconfigurable Array of Processors with Wider Bus Networks
ICPADS '97 Proceedings of the 1997 International Conference on Parallel and Distributed Systems
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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CAMP '95 Proceedings of the Computer Architectures for Machine Perception
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ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Information Processing Letters
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Structural Representation of Speech for Phonetic Classification
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Pattern Recognition Letters
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International Journal of Bio-Inspired Computation
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Expert Systems with Applications: An International Journal
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Journal of Experimental Algorithmics (JEA)
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IEEE Transactions on Evolutionary Computation
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Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
IEEE Transactions on Neural Networks
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This article is a survey into clustering applications and algorithms. A number of important well-known clustering methods are discussed. The authors present a brief history of the development of the field of clustering, discuss various types of clustering, and mention some of the current research directions in the field of clustering. More specifically, top-down and bottom-up hierarchical clustering are described. Additionally, K-Means and K-Medians clustering algorithms are also shown. The concept of representative points is introduced and the technique of discovering them is presented. Immense data sets in clustering often necessitate parallel computation. The authors discuss issues involving parallel clustering as well. Clustering deals with a large number of experimental results. The authors provide references to these works throughout the article. A table for comparing various clustering methods is given in the end. The authors give a summary and an extensive list of references, including some of the latest works in the field, to conclude the article.