Review on recent developments in frequent itemset based document clustering, its research trends and applications

Abstract

The document data is growing at an exponential rate. It is heterogeneous, dynamic and highly unstructured in nature. These characteristics of document data pose new challenges and opportunities for the development of various models and approaches for documents clustering. Different methods adopted for the development of these models. But these techniques have their advantages and disadvantages. The primary focus of the study is to the analysis of existing methods and approaches for document clustering based on frequent itemsets. Subsequently, this research direction facilitates the exploration of the emerging trends for each extension with applications. In this paper, more than 90 recent (published after 1990) research papers are summarised that are published in various reputed journals like IEEE Transaction, ScienceDirect, Springer-link, ACM and few fundamental authoritative articles.