The Passive Aggressive framework [1] is a principled approach to online linear classification that advocates minimal weight updates i.e., the least required so that the current tr...
Given a collection of Boolean spatio-temporal(ST) event types, the cascading spatio-temporal pattern (CSTP) discovery process finds partially ordered subsets of event-types whose ...
Pradeep Mohan, Shashi Shekhar, James A. Shine, Jam...
A relational probability tree (RPT) is a type of decision tree that can be used for probabilistic classification of instances with a relational structure. Each leaf of an RPT cont...
Given large, multi-million node graphs (e.g., FaceBook, web-crawls, etc.), how do they evolve over time? How are they connected? What are the central nodes and the outliers of the...
U. Kang, Charalampos E. Tsourakakis, Ana Paula App...
We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news ar...
B. Delaney, Andrew S. Fast, W. M. Campbell, C. J. ...
A highly skewed microdata contains some sensitive attribute values that occur far more frequently than others. Such data violates the "eligibility condition" assumed by ...
Yabo Xu, Ke Wang, Ada Wai-Chee Fu, Raymond Chi-Win...
We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework), can effectively incorporate...
Keith Henderson, Tina Eliassi-Rad, Spiros Papadimi...
We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of gra...
We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...