Incorporating background knowledge into data mining algorithms is an important but challenging problem. Current approaches in semi-supervised learning require explicit knowledge p...
Samah Jamal Fodeh, William F. Punch, Pang-Ning Tan
Numerous domains ranging from distributed data acquisition to knowledge reuse need to solve the cluster ensemble problem of combining multiple clusterings into a single unified cl...
— Cluster Ensembles is a framework for combining multiple partitionings obtained from separate clustering runs into a final consensus clustering. This framework has attracted mu...
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
In this paper, we propose a new algorithm for extending the hierarchical clustering methods and introduce a Multi-View Agglomerative Clustering approach to handle multi-view repre...