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
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
HARP: A Practical Projected Clustering Algorithm
IEEE Transactions on Knowledge and Data Engineering
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally adaptive metrics for clustering high dimensional data
Data Mining and Knowledge Discovery
Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests
Pattern Recognition Letters
A fuzzy subspace algorithm for clustering high dimensional data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Subspace clustering of text documents with feature weighting k-means algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Evolving soft subspace clustering
Applied Soft Computing
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Clustering technology has been used extensively for the analysis of gene expression data. Among various clustering methods, soft subspace clustering algorithms developed in recent years have demonstrated more promising performance than most traditional clustering algorithms and hard subspace clustering algorithms. Many soft subspace clustering algorithms have effectively utilized the within-cluster information, such as the within-cluster compactness, to develop the corresponding algorithms but few of them pay enough attention to other important information, such as the between-cluster information. Thus, it deserves further study to enhance soft subspace clustering by integrating more useful information in the clustering procedure. In this study, enhanced subspace clustering techniques are investigated for the clustering analysis of high dimensional gene expression data by integrating the within-cluster and between-cluster information simultaneously. First, a new optimization objective function is presented by integrating the fuzzy within-class compactness and the between-cluster separation in the weighting subspace. The corresponding learning rules for clustering are then derived based on the proposed objective function and a new soft subspace clustering algorithm, named as Enhanced Entropy-Weighting Subspace Clustering (EEW-SC), is proposed. The performance of the proposed algorithm on the clustering analysis of various high dimensional gene expression datasets is experimentally compared with that of several competitive subspace clustering algorithms. Our experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.