Data clustering using particle swarm optimization
DW Van der Merwe… - The 2003 Congress on …, 2003 - ieeexplore.ieee.org
DW Van der Merwe, AP Engelbrecht
The 2003 Congress on Evolutionary Computation, 2003. CEC'03., 2003•ieeexplore.ieee.orgThis paper proposes two new approaches to using PSO to cluster data. It is shown how PSO
can be used to find the centroids of a user specified number of clusters. The algorithm is
then extended to use K-means clustering to seed the initial swarm. This second algorithm
basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are
evaluated on six data sets, and compared to the performance of K-means clustering. Results
show that both PSO clustering techniques have much potential.
can be used to find the centroids of a user specified number of clusters. The algorithm is
then extended to use K-means clustering to seed the initial swarm. This second algorithm
basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are
evaluated on six data sets, and compared to the performance of K-means clustering. Results
show that both PSO clustering techniques have much potential.
This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.
ieeexplore.ieee.org