Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
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Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Relational Distance-Based Clustering
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ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
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IEEE Transactions on Neural Networks
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Kernel Canonical Correlation Analysis (KCCA) is a technique that can extract common features from a pair of multivariate data, which may assist in mining the ground truth hidden in the data. In this paper, a novel partitioning clustering method called KCK-means is proposed based on KCCA. We also show that KCK-means can not only be run on two-view data sets, but also it performs excellently on single-view data sets. KCK-means can deal with both binary-class and multi-class clustering tasks very well. Experiments with three evaluation metrics are also presented, the results of which reflect the promising performance of KCK-means.