Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Using Gaussians Functions to Determine Representative Clustering Prototypes
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Complexity reduction for "large image" processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
Various algorithms have been proposed for clustering large data sets for the hard and fuzzy case, not as much work has been done for automatic clustering approaches in which the number of clusters is unknown for the user. These approaches need some measures, called validity function to evaluate the clustering result and to give to the user the optimal number of clusters. In order to obtain this number, three conditions are necessary: (1) a good compression technique for data reduction with limited memory allocated, (b) good measures for the evaluation of the goodness of clusters for varying number of clusters, and (c) a good cluster algorithm that can automatically produce the number of clusters and takes into account the used compression technique. In this paper, we propose new clustering approaches which deals with new compression technique based on quality measures.