A comprehensive and analytical review of text clustering techniques

Document clustering involves grouping together documents so that similar documents are grouped together in the same cluster and different documents in the different clusters. Clustering of documents is considered a fundamental problem in the field of text mining. With a high rise in textual content over the Internet in recent years makes this problem more and more challenging. For example, SpringerNature has alone published more than 67,000 articles in the last few years just on the topic of COVID-19. This high volume leads to the challenge of very high dimensionality in analyzing the textual datasets. In this review paper, several text clustering techniques are reviewed and analyzed theoretically as well as experimentally. The reviewed techniques range from traditional non-semantic to some state-of-the-art semantic text clustering techniques. The individual performances of these techniques are experimentally compared and analyzed on several datasets using different performance measures such as purity, Silhouette coefficient, and adjusted rand index. Additionally, significant research gaps are also presented to give the readers a direction for future research.