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Clustering Approaches for Financial Data Analysis: a Survey
Author(s)
Date Issued
2012-07-19
Date Available
2016-09-02T10:25:54Z
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.
Type of Material
Conference Publication
Publisher
CSREA Press
Copyright (Published Version)
2012 CSREA Press
Web versions
Language
English
Status of Item
Peer reviewed
Part of
Abou-Nasr, M. and Arabnia, H. Proceedings of the International Conference on Data Mining (DMIN 2012), Las Vegas, Nevada, USA, 16-19 July 2012
Conference Details
International Conference on Data Mining (DMIN 2012), Las Vegas, Nevada, USA, 16-19 July 2012
This item is made available under a Creative Commons License
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