I. Introduction
Cluster analysis or clustering is the task of analyzing, categorizing, and grouping data into meaningful groups or clusters. Indeed, the data gathered in the same cluster show high similarity measures and the data in different clusters show high dissimilarity ones [1]. There are two basic types of clustering: hierarchical and partitional, with various algorithms. Noticeably, the partitional clustering algorithms acquired more attention because they are faster than hierarchical ones, and they offer strong assumptions in terms of their clustering quality, computational complexity, and parameters adjustment [2]. At their beginning, clustering algorithms required a big consumption of processing power due to the evolution of the huge volume of data. Hence, evaluating the computational complexity of these algorithms represents one of the most challenging issues in various domains. Recently, clustering based on heuristic approaches, specifically the ant colony clustering approach is most promising and has been shown satisfactory results in solving a wide variety of grouping problems [1],[3],[4]. Nonetheless, ant-based clustering algorithms applied with large datasets may represent a complex and time-consuming task. To deal with this research issue, we carried out a systematic literature review and try to give answers to the following Research Questions (RQs):