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
Fuzzy Sets and Systems
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Parallel Fuzzy c-Means Clustering for Large Data Sets
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Gradual model generator for single-pass clustering
Pattern Recognition
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
An enhanced possibilistic C-Means clustering algorithm EPCM
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Incremental clustering of dynamic data streams using connectivity based representative points
Data & Knowledge Engineering
Cluster-based under-sampling approaches for imbalanced data distributions
Expert Systems with Applications: An International Journal
Combining Multiple Interrelated Streams for Incremental Clustering
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Incremental spectral clustering by efficiently updating the eigen-system
Pattern Recognition
Fast Spectral Clustering with Random Projection and Sampling
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Incremental Document Clustering Based on Graph Model
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Adaptive Sampling for k-Means Clustering
APPROX '09 / RANDOM '09 Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
The ClusTree: indexing micro-clusters for anytime stream mining
Knowledge and Information Systems
Complexity reduction for "large image" processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
ASCCN: Arbitrary Shaped Clustering Method with Compatible Nucleoids
International Journal of Data Warehousing and Mining
Data Field for Hierarchical Clustering
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining
Hi-index | 0.00 |
Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach weighted fuzzy-possibilistic c-means, WFPCM is to use a modified possibilistic c-means PCM algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means FCM algorithm and the possibilistic c-means PCM algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes.