This paper introduces a set of algorithmic modifications that improve the partitioning results obtained with ant-based clustering. Moreover, general parameter settings and a self-...
Motivated by the principle of agnostic learning, we present an extension of the model introduced by Balcan, Blum, and Gupta [3] on computing low-error clusterings. The extended mod...
Self-Organizing Maps (SOM) are very powerful tools for data mining, in particular for visualizing the distribution of the data in very highdimensional data sets. Moreover, the 2D m...
Lowering computational cost of data analysis and visualization techniques is an essential step towards including the user in the visualization. In this paper we present an improve...
Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work e ectively and e ciently in highdimensional sp...