Partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means algorithm is best suited for implementing this operation becau...
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...
: Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerate...
Data mining applications place special requirements on clustering algorithms including: the ability to nd clusters embedded in subspaces of high dimensional data, scalability, end...
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopul...
The performance of graph based clustering methods critically depends on the quality of the distance function, used to compute similarities between pairs of neighboring nodes. In t...