Frequent Pattern Mining for Massive XBRL Data on Parallel FP-growth

被引：0
|
作者：
Feng, Tao [1 ]
Zeng, Zhi-Yong [1 ]
机构：
[1] Yunnan Univ Finance & Econ, Kunming 650000, Yunnan, Peoples R China
来源：
INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND COMMUNICATION ENGINEERING (CSCE 2015) | 2015年
关键词：
XBRL; FP-Growth; Frequent pattern; Parallel computing;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
XBRL is an XML-based extensible language for exchanging business information. The language is widely used in financial information disclosure system and becomes the standard data format of the system. The specification, taxonomy and instance documents of XBRL are researched in this paper and the method for the frequent pattern of massive XBRL data mining is proposed based on parallel FP-Growth. The XBRL instances of listed companies in China are processed by using this method and proved it to be effective.
引用
收藏
页码：1297 / 1305
页数：9
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