Now showing 1 - 2 of 2
  • Publication
    An integrated model for financial data mining
    Nowadays, financial data analysis is becoming increasingly importantin the business market. As companies collect more and more data fromdaily operations, they expect to extract useful knowledge from existing collecteddata to help make reasonable decisions for new customer requests, e.g. usercredit category, churn analysis, real estate analysis, etc. Financial institutes haveapplied different data mining techniques to enhance their business performance.However, simple approach of these techniques could raise a performance issue.Besides, there are very few general models for both understanding and forecastingdifferent financial fields. We present in this paper a new classification modelfor analyzing financial data. We also evaluate this model with different realworlddata to show its performance
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  • Publication
    Clustering Approaches for Financial Data Analysis: a Survey
    (CSREA Press, 2012-07-19) ; ;
    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.
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