TY - JOUR
T1 - Enhanced K-Means Clustering Algorithm using A Heuristic Approach
AU - Vighnesh Birodkar and Damodar Reddy Edla
JO - Journal of Information and Computing Science
VL - 4
SP - 277
EP - 284
PY - 2024
DA - 2024/01
SN - 9
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22571.html
KW - partitional clustering, K-means, unsupervised learning, cluster center, synthetic data 

AB - K-means algorithm is one of the most popular clustering algorithms that has been survived for 
more than 4 decades. Despite its inherent flaw of not knowing the number of clusters in advance, very few 
methods have been proposed in the literature to overcome it. The paper contains a fast heuristic algorithm for 
guessing the number of clusters as well as cluster center initialization without actually performing K-means, 
under  the  assumption  that  the  clusters  are  well  separated  in  a  certain  way.  The  proposed  algorithm  is 
experimented  on  various  synthetic  data.  The  experimental  results  show  the  effectiveness  of  the  proposed 
approach over the existing.