Clustering Approaches for Financial Data Analysis: a Survey

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Title: Clustering Approaches for Financial Data Analysis: a Survey
Authors: Le-Khac, Nhien-An
Cai, Fan
Kechadi, Tahar
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: ClusteringPartitioning clusteringDensity-based clusteringFinancial 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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