-
Procedia CS. 01/2012; 9:86-95.
-
[show abstract]
[hide abstract]
ABSTRACT: In this work, we present an interactive visual clustering approach for the exploration and analysis of vast volumes of data. Our proposed approach is a bio-inspired collective behavioral model to be used in a 3D graphics environment. Our paper illustrates an extension of the behavioral model for clustering and a parallel implementation, using Compute Unified Device Architecture to exploit the computational power of Graphics Processor Units (GPUs). The advantage of our approach is that, as data enters the environment, the user is directly involved in the data mining process. Our experiments illustrate the effectiveness and efficiency provided by our approach when applied to a number of real and synthetic data sets.
Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, part of the IEEE Symposium Series on Computational Intelligence 2011, April 11-15, 2011, Paris, France; 01/2011
-
2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris; 01/2011
-
[show abstract]
[hide abstract]
ABSTRACT: In this work, we present an interactive visual clustering approach for the exploration and analysis of datasets using the computational power of Graphics Processor Units (GPUs). The visualization is based on a collective behavioral model that enables cognitive amplification of information visualization. In this way, the workload of understanding the representation of information moves from the cognitive to the perceptual system. The results enable a more intuitive, interactive approach to the discovery of knowledge. The paper illustrates this behavioral model for clustering data, and applies it to the visualization of a number of real and synthetic datasets.
2011 15th International Conference on Information Visualisation, London; 01/2011
-
Motion in Games - Third International Conference, MIG 2010, Utrecht, The Netherlands, November 14-16, 2010. Proceedings; 01/2010
-
[show abstract]
[hide abstract]
ABSTRACT: In this work we describe a GPU implementation for an individual-based model for fish schooling. In this model each fish aligns its position and orientation with an appropriate average of its neighbors positions and orientations. This carries a very high computational cost in the so-called nearest neighbors search. By leveraging the GPU processing power and the new programming model called CUDA we implement an efficient framework which permits to simulate the collective motion of high-density individual groups. In particular we present as a case study a simulation of motion of millions of fishes. We describe our implementation and present extensive experiments which demonstrate the effectiveness of our GPU implementation.
