Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introd...
With the growing number of acquired physiological and behavioral biometric samples, biometric data sets are experiencing tremendous growth. As database sizes increase, exhaustive ...
Weintroduce a parallel approach, "DT-SELECT," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-SELECTis able to ra...
Clustering is an important technique for understanding and analysis of large multi-dimensional datasets in many scientific applications. Most of clustering research to date has be...
Clustering time series is a problem that has applications in a wide variety of fields, and has recently attracted a large amount of research. In this paper we focus on clustering...