A fast associative mining system based on search engine and concept graph for large-scale financial report texts

Author

Qian, Kun ; Hirokawa, Sachio ; Ejima, Kenji ; Du, Xiaoping

Author_Institution

Coll. of Software, Beihang Univ., Beijing, China

fYear

2010

fDate

17-19 Sept. 2010

Firstpage

675

Lastpage

679

Abstract

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.

Keywords

data mining; data visualisation; financial data processing; search engines; text analysis; concept graph; fast associative mining system; finance textual mining system; financial textual association mining; financial textual data analysis; large-scale financial report text; pattern discovery; search engine; text manipulation; visualization; Companies; Finance; Patents; Search engines; Sparse matrices; Text mining; Association Mining; Concept Graph; Financial Report; Financial Text Mining; Search Engine;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Financial Engineering (ICIFE), 2010 2nd IEEE International Conference on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-6927-7

Type

conf

DOI

10.1109/ICIFE.2010.5609447

Filename

5609447