An Application Based on K-Means Algorithm for Clustering Companies Listed

Author

Qian, YE

Author_Institution

Sch. of Finance, Zhejiang Univ. of Finance & Econ., Hangzhou

fYear

2006

fDate

21-23 June 2006

Firstpage

723

Lastpage

727

Abstract

There exist many customers in credit market that needs to be classified into distinct groups. K-means algorithm are presented, which based on the historical financial ratios, utilizing the cluster analysis technology to analyze the listed enterprises in Zhejiang province. Some indicators related to financial attributes are analyzed, and nine finance indicators are chosen. According to better valuation on the companies listed, we apply to "try and error" and choose 4 as the number of clustering. 81 samples are divided into two groups: one training group with 60 firms and other testing group with 21 samples. Testing results shows that the model trained can be available for clustering companies listed in Zhejiang province

Keywords

bank data processing; data mining; economic indicators; pattern clustering; statistical analysis; K-means clustering algorithm; Zhejiang province; banks; cluster analysis technology; credit market; data mining; finance indicators; historical financial ratios; listed enterprise analysis; testing group; training group; Algorithm design and analysis; Clustering algorithms; Companies; Cost accounting; Data mining; Finance; Financial management; Gaussian processes; Iterative algorithms; Space technology; K-Means Algorithm; clustering analysis; financial ratios; listed companies;

fLanguage

English

Publisher

ieee

Conference_Titel

Service Operations and Logistics, and Informatics, 2006. SOLI '06. IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

1-4244-0317-0

Electronic_ISBN

1-4244-0318-9

Type

conf

DOI

10.1109/SOLI.2006.329079

Filename

4125671