TITLE:
Particle Swarm Optimized Optimal Threshold Value Selection for Clustering based on Correlation Fractal Dimension
AUTHORS:
Anuradha Yarlagadda, J. V. R. Murthy, M. H. M. Krishna Prasad
KEYWORDS:
Correlation Fractal Dimension, Fractal Dimension, Clustering, Particle Swarm Optimization, Data Stream Clustering
JOURNAL NAME:
Applied Mathematics,
Vol.5 No.10,
June
17,
2014
ABSTRACT:
The
work on the paper is focused on the use of Fractal Dimension in clustering for
evolving data streams. Recently Anuradha et
al. proposed a new approach based on Relative Change in Fractal Dimension
(RCFD) and damped window model for clustering evolving data streams. Through observations on the aforementioned referred paper, this
paper reveals that the formation of quality cluster is heavily predominant on
the suitable selection of threshold value. In the above-mentionedpaper
Anuradha et al. have used a heuristic
approach for fixing the threshold value. Although the outcome of the approach
is acceptable, however, the approach is purely based on random selection and
has no basis to claim the acceptability in general. In this paper a novel
method is proposed to optimally compute threshold value using a population
based randomized approach known as particle swarm optimization (PSO). Simulations
are done on two huge data sets KDD Cup 1999 data set and the Forest Covertype
data set and the results of the cluster quality are compared with the fixed
approach. The comparison reveals that the chosen value of threshold by Anuradha et al., is robust and can be used
with confidence.