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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationProceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010
    Pages675-679
    Number of pages5
    DOIs
    Publication statusPublished - Dec 23 2010
    Event2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010 - Chongqing, China
    Duration: Sep 17 2010Sep 19 2010

    Publication series

    NameProceedings - 2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010

    Other

    Other2010 2nd IEEE International Conference on Information and Financial Engineering, ICIFE 2010
    CountryChina
    CityChongqing
    Period9/17/109/19/10

    All Science Journal Classification (ASJC) codes

    • Accounting
    • Finance

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