@Article{JICS-9-196,
author = {Yugal Kumar and G. Sahoo},
title = {A Hybrid Data Clustering Approach Based on Cat Swarm Optimization and K- Harmonic Mean Algorithm},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {9},
number = {3},
pages = {196--209},
abstract = {Clustering is an important task that is used to find subsets of similar objects from a set of objects 
such that the objects in the same subsets are more similar than other subsets. Large number of algorithms has 
been developed to solve the clustering problem.  K-Harmonic Mean (KHM) is one of the  popular technique 
that  has  been  applied  in  clustering  as  a  substitute  of  K-Means  algorithm  because  it  is  insensitive  to 
initialization issues due to built in boosting function. But, this method is also trapped in local optima. On the 
other  hand,  Cat  Swarm  Optimization  (CSO)  is  the  latest  population  based  optimization  method  used  for 
global  optimization.  In  this  paper  a  hybrid  data  clustering  method  is  proposed  based  on  CSO  and  KHM 
which includes the  advantage of both algorithms  and named  as CSOKHM.  The hybrid CSOKHM not only 
improved  the  convergence  speed  of  CSO  but  also  escape  the  KHM  method  to  run  in  local  optima.  The 
performance of the CSOKHM is evaluated using seven datasets and compared with KHM, PSO, PSOKHM, 
ACA,  ACAKHM,  GSAKHM, CSO methods. The experimental results show the applicability of CSOKHM 
method.. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22580.html}
}