We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
Privacy-preserving data mining (PPDM) is an emergent research area that addresses the incorporation of privacy preserving concerns to data mining techniques. In this paper we prop...
Summarization is an important task in data mining. A major challenge over the past years has been the efficient construction of fixed-space synopses that provide a deterministic q...
Correlation mining has gained great success in many application domains for its ability to capture the underlying dependency between objects. However, the research of correlation ...
The problem of assessing the significance of data mining results on high-dimensional 0?1 data sets has been studied extensively in the literature. For problems such as mining freq...
Aristides Gionis, Heikki Mannila, Panayiotis Tsapa...