Active Data Clustering
Active data clustering is a novel technique for clustering of proximity data which utilizes principles from sequential experiment design in order to interleave data generation and data analysis. The proposed active data sampling strategy is based on the expected value of information, a concept rooting in statistical decision theory. This is considered to be an important step towards the analysis of large-scale data sets, because it offers a way to overcome the inherent data sparseness of proximity data. We present applications to unsupervised texture segmentation in computer vision and information retrieval in document databases.