Clustering Approaches for Financial Data Analysis: a Survey

TL;DR

This survey reviews various clustering algorithms applied to financial data analysis, highlighting their strengths and weaknesses in extracting meaningful structures from diverse financial datasets to support decision-making.

Contribution

It provides a comprehensive evaluation of clustering methods specifically tailored for financial data, an area with limited prior focused studies.

Findings

Different clustering algorithms have varying effectiveness on financial datasets.

Clustering techniques can reveal underlying data structures important for financial decision-making.

The paper discusses the pros and cons of each method in financial contexts.

Abstract

Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, confidence of expected return, etc. Banking and financial institutes have applied different data mining techniques to enhance their business performance. Among these techniques, clustering has been considered as a significant method to capture the natural structure of data. However, there are not many studies on clustering approaches for financial data analysis. In this paper, we evaluate different clustering algorithms for analysing different financial datasets varied from time series to transactions. We also discuss the advantages and disadvantages of each method to enhance the understanding of inner structure of financial datasets as well as the capability of each clustering method in this context.