Predictive modelling of online dynamic user-interaction recordings and community identifi cation from such data b ecomes more and more imp ortant w ith th e w idesp read use of on...
Rules are commonly used for classification because they are modular, intelligible and easy to learn. Existing work in classification rule learning assumes the goal is to produce ca...
Abstract. Data stream values are often associated with multiple aspects. For example, each value observed at a given time-stamp from environmental sensors may have an associated ty...
Jimeng Sun, Charalampos E. Tsourakakis, Evan Hoke,...
Abstract. In this paper we aim at extending the non-derivable condensed representation in frequent itemset mining to sequential pattern mining. We start by showing a negative examp...
Learning from imbalanced datasets presents a convoluted problem both from the modeling and cost standpoints. In particular, when a class is of great interest but occurs relatively...
Nitesh V. Chawla, David A. Cieslak, Lawrence O. Ha...
Abstract We address the problem of monitoring and identification of correlated burst patterns in multi-stream time series databases. We follow a two-step methodology: first we iden...