SynDECA: A Tool to Generate Synthetic Datasets for Evaluation of Clustering Algorithms Jhansi Rani Vennam and Soujanya Vadapalli   View Paper (PDF)   Return to Data Mining: Session-I Abstract A large number of clustering algorithms have been proposed of late, which can identify clusters of arbitrary shapes, varying densities and sizes. There are no benchmarking datasets with high dimensionality and noise, which can evaluate clustering algorithms on various aspects like scalability, accuracy and robustness to noise. Real-life datasets are few in number and do not have the "original" clustering results by default. This emphasizes the need to have a toolkit that can generate datasets, which mimic real-life data and provides the actual clustering results. In this paper, we propose few algorithms and methodologies that generate high dimensional datasets along with the original clustering results. We developed a toolkit called SynDECA that generates synthetic datasets based on the algorithms proposed. ©2006 Association for Computing Machinery