CLUMP: A Scalable and Robust Framework for Structure Discovery Kunal Punera and Joydeep Ghosh   View Paper (PDF)   Return to Session 2: Clustering Schemes I Abstract We introduce a robust and efficient framework called CLUMP (CLustering Using Multiple Prototypes) for unsupervised discovery of structure in data. CLUMP relies on finding multiple prototypes that summarize the data. Clustering the prototypes enables our algorithm to scale up to extremely large and high-dimensional domains such as text data. Other desirable properties include robustness to noise and parameter choices. In this paper, we describe the approach in detail, characterize its performance on a variety of datasets, and compare it to some existing model selection approaches. ©2006 Association for Computing Machinery