WIT Press


K-means Algorithm And Its Application For Clustering Companies Listed In Zhejiang Province

Price

Free (open access)

Paper DOI

10.2495/DATA060041

Volume

37

Pages

10

Published

2006

Size

1,067 kb

Author(s)

Y. Qian

Abstract

There exist many problems in the credit market where we have data that needs to be classified into distinct groups. This paper will introduce a financial K-means algorithm, which based on the historical financial ratios, applies the cluster analysis technology to analyze the listed enterprises in Zhejiang province. We analyze indicators related to financial attributes and choose nine finance indicators. According to better valuation on the companies listed, we apply \“trial and error” and choose four as the number of clustering. Testing shows that companies belong to cluster 2 and cluster 3 add up to 71 companies, including 87% in all. They are all companies worthy of making loans, which is inconsistent with the good economic situation of Zhejiang province. Category 4 has nine companies including 11% that are judged as high risk business. So banks should provide these customers for loans with a mortgage or guarantee. Keywords: K-means algorithm, clustering analysis, financial ratios, listed companies. 1 Introduction In credit market, banks want to analyze the customer’s preferences to make loan decision, to offer loans and set loan rate, and to decide their market strategy, and to provide customized guide to their potential customers [1]. In today’s information based society, there is an urge for bank managers have only vague idea, to find the needed information from the overwhelming resources, who is a good client and who is a bad client (whom to watch carefully to minimize the bank loses. some company’s financial reports contain a lot of

Keywords

K-means algorithm, clustering analysis, financial ratios, listed companies.