Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
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
A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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 new cell-based clustering method for large, high-dimensional data in data mining applications
Proceedings of the 2002 ACM symposium on Applied computing
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Improving Performance of Similarity-Based Clustering by Feature Weight Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Locally adaptive metrics for clustering high dimensional data
Data Mining and Knowledge Discovery
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
Computational Statistics & Data Analysis
Feature selection with dynamic mutual information
Pattern Recognition
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective genetic algorithm-based fuzzy clustering of categorical attributes
IEEE Transactions on Evolutionary Computation
Data Clustering Using Multi-objective Differential Evolution Algorithms
Fundamenta Informaticae
A new multiobjective clustering technique based on the concepts of stability and symmetry
Knowledge and Information Systems
A unifying criterion for unsupervised clustering and feature selection
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
OFFSS: optimal fuzzy-valued feature subset selection
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.01 |
Many conventional soft subspace clustering techniques merge several criteria into a single objective to improve performance; however, the weighting parameters become important but difficult to set. In this paper, a novel soft subspace clustering with a multi-objective evolutionary approach (MOEASSC) is proposed to this problem. This clustering method considers two types of criteria as multiple objectives and optimizes them simultaneously by using a modified multi-objective evolutionary algorithm with new encoding and operators. An indicator called projection similarity validity index (PSVIndex) is designed to select the best solution and cluster number. Experiments on many datasets demonstrate the usefulness of MOEASSC and PSVIndex, and show that our algorithm is insensitive to its parameters and is scalable to large datasets.