TY - GEN
T1 - Dimensionality reduction particle swarm algorithm for high dimensional clustering
AU - Cui, Xiaohui
AU - Beaver, Justin M.
AU - Charles, Jesse St
AU - Potok, Thomas E.
PY - 2008
Y1 - 2008
N2 - The Particle Swarm Optimization (PSO) clustering algorithm can generate more compact clustering results than the traditional K-means clustering algorithm. However, when clustering high dimensional datasets, the PSO clustering algorithm is notoriously slow because its computation cost increases exponentially with the size of the dataset dimension. Dimensionality reduction techniques offer solutions that both significantly improve the computation time, and yield reasonably accurate clustering results in high dimensional data analysis. In this paper, we introduce research that combines different dimensionality reduction techniques with the PSO clustering algorithm in order to reduce the complexity of high dimensional datasets and speed up the PSO clustering process. We report significant improvements in total runtime. Moreover, the clustering accuracy of the dimensionality reduction PSO clustering algorithm is comparable to the one that uses full dimension space.
AB - The Particle Swarm Optimization (PSO) clustering algorithm can generate more compact clustering results than the traditional K-means clustering algorithm. However, when clustering high dimensional datasets, the PSO clustering algorithm is notoriously slow because its computation cost increases exponentially with the size of the dataset dimension. Dimensionality reduction techniques offer solutions that both significantly improve the computation time, and yield reasonably accurate clustering results in high dimensional data analysis. In this paper, we introduce research that combines different dimensionality reduction techniques with the PSO clustering algorithm in order to reduce the complexity of high dimensional datasets and speed up the PSO clustering process. We report significant improvements in total runtime. Moreover, the clustering accuracy of the dimensionality reduction PSO clustering algorithm is comparable to the one that uses full dimension space.
UR - http://www.scopus.com/inward/record.url?scp=57649158471&partnerID=8YFLogxK
U2 - 10.1109/SIS.2008.4668309
DO - 10.1109/SIS.2008.4668309
M3 - Conference contribution
AN - SCOPUS:57649158471
SN - 9781424427055
T3 - 2008 IEEE Swarm Intelligence Symposium, SIS 2008
BT - 2008 IEEE Swarm Intelligence Symposium, SIS 2008
T2 - 2008 IEEE Swarm Intelligence Symposium, SIS 2008
Y2 - 21 September 2008 through 23 September 2008
ER -