Data clustering using efficient similarity measures

The need for appropriate applications of the various similarity measures for clustering has arisen over the years as data massively keep on increasing. The issue of deciding which similarity measure is the best and on what kind of dataset have been a very cumbersome task in the field of data mining, data science, other related fields, and organizations that highly depends on the knowledge outcome from a huge set of data to make some vital / crucial decisions. This is because various datasets portray some common features associated with them; the need for clearer understanding of the various similarity measures for clustering different datasets is needed. This paper presents a critical review of various similarity measures applied in text and data clustering. A theoretical comparison has been made to check the suitability of the measures on different kind of data sets.