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
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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new unsupervised approach for fuzzy clustering
Fuzzy Sets and Systems
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
Generalized weighted conditional fuzzy clustering
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.20 |
Recently many researchers exert their effort on clustering as a primary data mining method for knowledge discovery, but only few of them have focused on uneven dataset. In the last research, we proposed an efficient hierarchical algorithm based on fuzzy graph connectedness-FHC-to discover clusters with arbitrary shapes. In this paper, we present a novel clustering algorithm for uneven dataset-PFHC-which is an extended version based on FHC. In PFHC, dataset is divided into several local spaces firstly according to the data density of distribution, where the data density in any local space is nearly uniform. In order to achieve the goal, local @? and @l are used in each local domain to acquire local clustering result by FHC. Then boundary between local areas needs being taken into consideration for combination. Finally local clusters need to be merged to obtain global clusters. As an extension of FHC, PFHC can deal with uneven datasets more effectively and efficiently, and generate better quality clusters than other methods as experiment shows. Furthermore, PFHC is found to be able to process incremental data as well in this work.