A Fast Associative Mining System Based on Search Engine and Concept Graph for Large-Scale Financial Report Texts

摘要：

Association mining is widely used in pattern discovery.For large scale financial textual data analysis,however,association mining is relatively less applied due to low efficiency in text manipulation.This paper presents a fast finance textual mining system,based on search engine and concept graph,for large scale financial textual association mining and visualization.Through the experiments on ten years financial reports of 6,049 companies from NASDAQ and NYSE from 1999 to 2008,it testified that this system could rapidly extracting the characteristic words among millions of texts and visualizing them by concept graph in seconds.

关键词： Financial Text Mining Financial Report Concept Graph Search Engine Association Mining

作者: Kun Qian Kenji Ejima Sachio Hirokawa Xiaoping Du

作者单位: College of Software Beihang University Beijing,China Lafla Inc.Fukuoka,Japan Research Institute for Information Technology Kyushu University Fukuoka,Japan

会议类型: 国际会议

会议名称: 2010 2nd IEEE International Conference on Information and Financial Engineering(2010年第二届IEEE信息与金融工程国际会议 ICIFE 2010)

会议地点: 重庆

会议语种:英文

页码: 675-679

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）