Improved visual clustering of large multi-dimensional data sets

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 improved algorithm for visual clustering of large multi-dimensional data sets. The original algorithm is an approach that deals efficiently with multi-dimensionality using various projections of the data in order to perform multi-space clustering, pruning outliers through direct user interaction. The algorithm presented here, named HC-Enhanced (for human-computer enhanced), adds a scalability level to the approach without reducing clustering quality. Additionally, an algorithm to improve clusters is added to the approach. A number of test cases is presented with good results.