中国科学院软件研究所机构知识库
Register
Email:
Passwd
验 证:
换一张
Have you forgotten your password? Log In
Advanced  
  Communities
  Subjects
  All Items
ISCAS OpenIR  > 软件所图书馆  > 期刊论文
Subject: Computer Science, Artificial Intelligence
Title:
learning quantifiable associations via principal sparse non-negative matrix factorization
Author: Hu Chenyong ; Zhang Benyu ; Wang Yongji ; Yan Shuicheng ; Chen Zheng ; Wang Qing ; Yang Qiang
Keyword: data mining ; association rules ; non-negative matrix factorization ; principal sparse non-negative matrix factorization
Source: INTELLIGENT DATA ANALYSIS
Issued Date: 2005
Volume: 9, Issue:6, Pages:603-620
Indexed Type: sci
Department: Chinese Acad Sci, Inst Software, Lab Internet Software Technol, Beijing 100080, Peoples R China. Microsoft Res Asia, Beijing 100080, Peoples R China. Chinese Univ Hong Kong, Dept Informat Engn, Hong Kong, Hong Kong, Peoples R China. Hong Kong Univ Sci & Technol, Dept Comp Sci, Sha Tin, Hong Kong, Peoples R China. Chinese Acad Sci, Inst Software, Key Lab Comp Sci, Beijing, Peoples R China.
Language: 英语
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/12482
Appears in Collections:软件所图书馆_期刊论文

Files in This Item:
File Name/ File Size Content Type Version Access License
hu.pdf（153KB）----限制开放-- 联系获取全文

Recommended Citation:
Hu Chenyong,Zhang Benyu,Wang Yongji,et al. learning quantifiable associations via principal sparse non-negative matrix factorization[J]. INTELLIGENT DATA ANALYSIS,2005-01-01,9(6):603-620.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Hu Chenyong]'s Articles
[Zhang Benyu]'s Articles
[Wang Yongji]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Hu Chenyong]‘s Articles
[Zhang Benyu]‘s Articles
[Wang Yongji]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放，请您登录
您对该条目有什么异议，请填写以下表单，管理员会尽快联系您。
内 容：
Email：  *
单位：
验证码：   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题：
  * 
内 容：
Email：  *
验证码：   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 
Valid XHTML 1.0!
Copyright © 2007-2019  中国科学院软件研究所 - Feedback
Powered by CSpace