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 t...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Yi Liu, Rong Jin, Anil K. Jain
• Keywords: Data Clustering, Data Mining, KDD 2007, Popular Clustering Algorithms, Special Clustering Algorithms