ncts

Spectral clustering has emerged as a very effective clustering approach; however, it is computationally very expensive. As a result, there has been considerable effort in the machine learning community to develop fast, approximate spectral clustering algorithms that are scalable in time to large data. Notably, most of those methods use a small set of landmark points selected from the given data. In this talk we present two new landmark-based scalable spectral clustering algorithms that are developed based on novel document-term and bipartite graph models. We demonstrate the superior performance of our proposed algorithms by comparing them with the state-of-the-art methods on some benchmark data sets. Finally, we address the setting of massive data sets which cannot be fully loaded into computer memory and present some ongoing work.