A Review of Research on Financial Time Series Clustering: A Bibliometrics Approach

The amount of information and data we retrieve and use is growing rapidly. Data mining is the process of extracting relevant data from large volumes of data and the method of discovering and finding the appropriate pattern from large volumes of data sets. Clustering is one of the most common methods of statistical data analysis, and also one of the best data mining approaches. This approach, as a method of unsupervised learning, uses algorithms to classify time series data according to different criteria. The purpose of this study is to investigate the types of applications of clustering and networking in various financial fields, including risk, algorithmic trading, banking and other widely used topics in this field. In this research, using the bibliometrix package in the software, all the researches on clustering is reviewed. While extracting various criteria and clustering approaches, its applications have been studied. This study with a comprehensive review of all research in this field can help researchers as a toolbox to provide a variety of clustering methods in ideation and selection of appropriate methods in classifying and analyzing financial data.