The paper describes results of an empirical study, where some hypotheses about the impact of reuse on defect-density and stability, and about the impact of component size on defec...
Parastoo Mohagheghi, Reidar Conradi, Ole M. Killi,...
Boosting is a simple yet powerful modeling technique that is used in many machine learning and data mining related applications. In this paper, we propose a novel scale-space based...
Spectral analysis approaches have been actively studied in machine learning and data mining areas, due to their generality, efficiency, and rich theoretical foundations. As a natur...
Dijun Luo, Heng Huang, Chris H. Q. Ding, Feiping N...
Graph clustering (also called graph partitioning) -- clustering the nodes of a graph -- is an important problem in diverse data mining applications. Traditional approaches involve...
Time series motifs are pairs of individual time series, or subsequences of a longer time series, which are very similar to each other. As with their discrete analogues in computat...
Abdullah Mueen, Eamonn J. Keogh, M. Brandon Westov...