Dimensionality reduction particle swarm algorithm for high dimensional clustering

Xiaohui Cui, Justin M. Beaver, Jesse St Charles, Thomas E. Potok

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2008 IEEE Swarm Intelligence Symposium, SIS 2008
DOIs
StatePublished - 2008
Event2008 IEEE Swarm Intelligence Symposium, SIS 2008 - St. Louis, MO, United States
Duration: Sep 21 2008Sep 23 2008

Publication series

Name2008 IEEE Swarm Intelligence Symposium, SIS 2008

Conference

Conference2008 IEEE Swarm Intelligence Symposium, SIS 2008
Country/TerritoryUnited States
CitySt. Louis, MO
Period09/21/0809/23/08

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