Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clusteri...
Graph clustering (also called graph partitioning) -- clustering the nodes of a graph -- is an important problem in diverse data mining applications. Traditional approaches involve...
Clustering stability is an increasingly popular family of methods for performing model selection in data clustering. The basic idea is that the chosen model should be stable under...
By analogy with merging documents rankings, the outputs from multiple search results clustering algorithms can be combined into a single output. In this paper we study the feasibi...
Background: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-pro...
Jean-Philippe Vert, Jian Qiu, William Stafford Nob...