A study on the analysis of stability indicators in financial statements using Fuzzy C-Means clustering
- Authors
- Kim, M.S.; Choi, E.S.; Lee, J.Y.; Kang, M.S.
- Issue Date
- 2017
- Publisher
- Research India Publications
- Keywords
- Clustering; Fuzzy C-Means; Machine learning; Stability indicators
- Citation
- International Journal of Applied Engineering Research, v.12, no.20, pp 9863 - 9865
- Pages
- 3
- Journal Title
- International Journal of Applied Engineering Research
- Volume
- 12
- Number
- 20
- Start Page
- 9863
- End Page
- 9865
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/6090
- ISSN
- 0973-4562
- Abstract
- IoT and A.I researches are active as the 4th industrial revolution comes. Innovative companies have already applied artificial intelligence to some functions. In addition, there will be an increasing number of companies that apply artificial intelligence to business analysis, and in the future, business management such as management and management finance experts will be replaced by artificial intelligence. In this paper, we selected 334 corporations which are corporations with total assets of 12 billion more out of the list specified by Korea Information & Communication Construction Association. We calculated the debt ratio, current ratio, BIS ratio from financial statement. These three things are stability indices. The debt ratio, the current ratio, and BIS ratio, which are key business analysis indicators of the stability indices, are clustered and analyzed by applying the Fuzzy C-Means clustering algorithm based on Fuzzy theory. As a result of clustering, we could see that it was divided into a certain section. © Research India Publications.
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