A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
SCHISM: A New Approach for Interesting Subspace Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Cluster Analysis for Data Mining and System Identification
Cluster Analysis for Data Mining and System Identification
DUSC: Dimensionality Unbiased Subspace Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
EDSC: efficient density-based subspace clustering
Proceedings of the 17th ACM conference on Information and knowledge management
| Hi-index | 0.00 |
Fuzzy techniques have been used for handling vague boundaries of arbitrarily oriented cluster structures. However, traditional clustering algorithms tend to break down in high dimensional spaces due to inherent sparsity of data. In order to model the uncertainties of high dimensional data, we propose modification of objective functions of Gustafson Kessel algorithm for subspace clustering, through automatic selection of weight vectors and present the results of applying the proposed approach to UCI data sets.