@Article{2019.100000155,
AUTHOR = {Parvathavarthini S, Karthikeyani Visalakshi N, Shanthi S, Madhan Mohan J},
TITLE = {An Improved Crow Search Based Intuitionistic Fuzzy Clustering Algorithm  for Healthcare Applications},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {2},
PAGES = {253--260},
URL = {http://www.techscience.com/iasc/v26n2/39947},
ISSN = {2326-005X},
ABSTRACT = {Intuitionistic fuzzy clustering allows the uncertainties in data to be represented 
more precisely. Medical data usually possess a high degree of uncertainty and 
serve as the right candidate to be represented as Intuitionistic fuzzy sets. 
However, the selection of initial centroids plays a crucial role in determining the 
resulting cluster structure. Crow search algorithm is hybridized with 
Intuitionistic fuzzy C-means to attain better results than the existing hybrid 
algorithms. Still, the performance of the algorithm needs improvement with 
respect to the objective function and cluster indices especially with internal 
indices. In order to address these issues, the crow search algorithm is modified 
by introducing the genetic operators like cloning and mutation operators. In 
addition to that, an archive of memory is created to store the best solutions of 
the iterations and these values are used for updating the position when the 
acquired solutions are not feasible. The results obtained are compared with 
other hybrid Intuitionistic fuzzy C-means algorithms and the performance of 
ICrSA-IFCM is high in terms of the objective function and cluster validity 
indices.},
DOI = {10.31209/2019.100000155}
}