Applied multivariate statistical analysis
Applied multivariate statistical analysis
OPTICS: ordering points to identify the clustering structure
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
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Cluster ensemble technique has attracted serious attention in the area of unsupervised learning. It aims at improving robustness and quality of clustering scheme, particularly in scenarios where either randomization or sampling is the part of the clustering algorithm. In this paper, we address the problem of instability and non robustness in K-means clusterings. These problems arise naturally because of random seed selection by the algorithm, order sensitivity of the algorithm and presence of noise and outliers in data. We propose a cluster ensemble method based on Discriminant Analysis to obtain robust clustering using K-means clusterer. The proposed algorithm operates in three phases. The first phase is preparatory in which multiple clustering schemes generated and the cluster correspondence is obtained. The second phase uses discriminant analysis and constructs a label matrix. In the final stage, consensus partition is generated and noise, if any, is segregated. Experimental analysis using standard public data sets provides strong empirical evidence of the high quality of resultant clustering scheme.