Random walk graph kernel has been used as an important tool for various data mining tasks including classification and similarity computation. Despite its usefulness, however, it...
Clustering of high dimensional data streams is an important problem in many application domains, a prominent example being network monitoring. Several approaches have been lately ...
Irene Ntoutsi, Arthur Zimek, Themis Palpanas, Peer...
In this article, we propose a random walk-based model to predict legislators’ votes on a set of bills. In particular, we first convert roll call data, i.e. the recorded votes a...
Compression based pattern mining has been successfully applied to many data mining tasks. We propose an approach based on the minimum description length principle to extract seque...
One practical inconvenience in frequent pattern mining is that it often yields a flood of common or uninformative patterns, and thus we should carefully adjust the minimum suppor...
In this paper, we consider the multi-task sparse learning problem under the assumption that the dimensionality diverges with the sample size. The traditional l1/l2 multi-task lass...
Xi Chen, Jingrui He, Rick Lawrence, Jaime G. Carbo...
The Knowledge Discovery Toolbox (KDT) enables domain experts to perform complex analyses of huge datasets on supercomputers using a high-level language without grappling with the ...
A Cyber-Physical System (CPS) integrates physical (i.e., sensor) devices with cyber (i.e., informational) components to form a situation-aware system that responds intelligently t...
Lu An Tang, Quanquan Gu, Xiao Yu, Jiawei Han, Thom...
We propose a new matrix completion algorithm— Kernelized Probabilistic Matrix Factorization (KPMF), which effectively incorporates external side information into the matrix fac...
Early stress recognition is of great relevance in precision plant protection. Pre-symptomatic water stress detection is of particular interest, ultimately helping to meet the chal...
Kristian Kersting, Mirwaes Wahabzada, Christoph R&...