Improving K means through better initialization and normalization
K means is still a popular clustering algorithm and active research area. The research is majorly focused at improving efficiency and effectiveness of the method. This paper proposes combined approach of a ranked initialization and normalization of data values with k means. Three variations of a score based initialization approach is proposed. Experiments are performed on normalized data to prove the superiority of the proposed algorithm.
2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)