  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.
