Clustering is performed to get insights into the data whose volume makes it problematic for analysis by humans. Due to this, clustering algorithms have emerged as meta learning tools for performing exploratory data analysis. A Cluster is defined as a set of objects which have a higher degree of similarity to each other compared to objects not in the same set. However there is ambiguity regarding a suitable similarity metric for clustering. Multiple measures have been proposed related to quantifying similarity such as euclidean distance, density in data space etc. making clustering a multi-objective optimization problem. In this paper, different clustering approaches are studied from the theoretical perspective to understand their relevance in context of massive data-sets and empirically these have been tested on artificial benchmarks to highlight their strengths and weaknesses.
Nerurkar, P., Shirke, A., Chandane, M., & Bhirud, S. (2018). Empirical Analysis of Data Clustering Algorithms. In Procedia Computer Science (Vol. 125, pp. 770–779). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.12.099