document: BoostCluster : boosting clustering by pairwise constraints Data clustering is an important task in many disciplines . A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints . However , these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints . We present a boosting framework for data clustering , termed as BoostCluster , that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints . The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised . The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are , on the one hand , adapted to the clustering results at previous iterations by the given algorithm , and on the other hand consistent with the given side information . Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms ( K-means , partitional SingleLink , spectral clustering ) , and its performance is comparable to the state-of-the-art algorithms for data clustering with side information .
doc_bio_tags: O O B O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O