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Affinity Propagation Clustering with Background Knowledge using Pair Wise Constraints

M. Yoga, R. Vadivu


The pairwise constraints specifying whether a pair of samples should be grouped together or not have been successfully incorporated into the conventional clustering methods such as k-means and spectral clustering for the performance enhancement. Nevertheless, the issue of pairwise constraints has not been well studied in the recently proposed MMC (Maximum Margin Clustering), which extends the MMC in supervised learning for clustering and often shows a promising performance. In clustering process, semi-supervised learning is a class of machine learning techniques that make use of small amount of labeled and large amount of unlabeled data for training .This affinity propagation is aimed in making the effective clustering process by performing word wise comparison and overcomes the problem of overlapping that is encountered in K-mean and reduces the memory space required.


Affinity Propagation, Pair wise Constraints, Semi Supervised Learning, Word Pair Process, MMC

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