Incorporating background knowledge into data mining algorithms is an important but challenging problem. Current approaches in semi-supervised learning require explicit knowledge p...
Samah Jamal Fodeh, William F. Punch, Pang-Ning Tan
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
The assessment of the reliability of clusters discovered in bio-molecular data is a central issue in several bioinformatics problems. Several methods based on the concept of stabil...
Abstract. Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. Various heuristics for constructing such ...