Learn More
For high-dimensional regression, the number of predictors may greatly exceed the sample size but only a small fraction of them are related to the response. Therefore, variable selection is inevitable, where consistent model selection is the primary concern. However, conventional consistent model selection criteria like BIC may be inadequate due to their(More)
Due to the advances in remote sensing and sensor networks, different types of dynamic and spatio-temporal datasets become increasingly available. Extracting spatial and temporal patterns from such datasets is very important as it has many applications, such as understanding climate change, geo-targeting, and environment protection. Traditional clustering(More)
From a nanoscience perspective, cellular processes and their reduced in vitro imitations provide extraordinary examples for highly robust few or single molecule reaction pathways. A prime example are biochemical reactions involving DNA molecules, and the coupling of these reactions to the physical conformations of DNA. In this review, we summarise recent(More)
Spatio-temporal clustering aims to discover interesting regions in spatio-temporal data. In this paper, we propose a novel, serial, density-contour based spatio-temporal clustering algorithm called ST-DCONTOUR which employs a model-based clustering methodology to obtain spatio-temporal clusters from location streams. Our approach subdivides the incoming(More)
Correlation is an important statistical measure for estimating dependencies between numerical attributes in multivariate datasets. Previous correlation discovery algorithms mostly dedicate to find piece-wise correlations between the attributes. Other research efforts, such as correlation preserving discretization, can find strongly correlated intervals(More)