Clustering Approaches for Financial Data Analysis: a Survey
|Title:||Clustering Approaches for Financial Data Analysis: a Survey||Authors:||Le-Khac, Nhien-An
|Permanent link:||http://hdl.handle.net/10197/7851||Date:||19-Jul-2012||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||Keywords:||Clustering; Partitioning clustering; Density-based clustering; Financial datasets||Language:||en||Status of Item:||Peer reviewed||Is 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|
|Appears in Collections:||Computer Science Research Collection|
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