Clustering is a technique of keeping the closely related or in other words similar data into groups. Clustering is mainly a process in which a given data set is partitioned into homogenous groups on the basis of certain features like the similar objects are clustered into one group and dissimilar objects are clustered into another group. Therefore, clustering is a type of unsupervised learning and not a supervised learning approach like classification. Different types of clustering techniques are available such as partitioning, density- based, grid-based, hierarchical, model-based and soft-computing methods. In this paper, we propose a comparative study between K-Means and hierarchical clustering methods, claiming that the quality of hierarchical clustering increases as compared to K-Means clustering for the same number of iterations and splitting percentage, as the clustered instances will be more for hierarchical clustering. When performance is considered, K-Means stands over hierarchical clustering. Also we propose that when data is transformed by normalization which is a data preprocessing task results in improved accuracy and quality of Hierarchical clustering as of now.
K. T. Athira, Gopika, P. G., and G. Deepa, “Collation Between Hierarchical and K- Means Clustering Algorithm”, Journal of Engineering and Applied Sciences, 2018.