Algorithms for clustering data
Algorithms for clustering data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
A new cell-based clustering method for large, high-dimensional data in data mining applications
Proceedings of the 2002 ACM symposium on Applied computing
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Multi-Objective Clustering Ensemble
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Locally adaptive metrics for clustering high dimensional data
Data Mining and Knowledge Discovery
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
A new method for hierarchical clustering combination
Intelligent Data Analysis
A heuristically perturbation of dataset to achieve a diverse ensemble of classifiers
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Unsupervised linkage learner based on local optimums
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
A heuristic diversity production approach
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
A clustering ensemble based on a modified normalized mutual information metric
AMT'12 Proceedings of the 8th international conference on Active Media Technology
A diversity production approach in ensemble of base classifiers
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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Conventional clustering algorithms employ a set of features; each feature participates in the clustering procedure equivalently. Recently this problem is dealt with by Locally Adaptive Clustering, LAC. However, like its traditional competitors the LAC method suffers from inefficiency in data with unbalanced clusters. In this paper a novel method is proposed which deals with the problem while it preserves LAC privilege. While LAC forces the sum of weights of the clusters to be equal, our method let them be unequal. This makes our method more flexible to conquer over falling at the local optimums. It also let the cluster centers to be more efficiently located in fitter places than its rivals.