A fast associative mining system based on search engine and concept graph for large-scale financial report texts
Kun Qian, Sachio Hirokawa, Kenji Ejima, Xiaoping Du · 2010
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.