检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

146 篇 会议
87 篇 期刊文献
1 篇 学位论文

馆藏范围

234 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

216 篇 工学
- 195 篇 计算机科学与技术...
- 57 篇 电气工程
- 27 篇 软件工程
- 24 篇 控制科学与工程
- 14 篇 信息与通信工程
- 7 篇 仪器科学与技术
- 4 篇 电子科学与技术（可...
- 2 篇 机械工程
- 2 篇 材料科学与工程（可...
- 2 篇 网络空间安全
- 1 篇 冶金工程
- 1 篇 建筑学
- 1 篇 石油与天然气工程
- 1 篇 生物医学工程（可授...
- 1 篇 食品科学与工程（可...
- 1 篇 城乡规划学
- 1 篇 生物工程
35 篇 管理学
- 31 篇 管理科学与工程(可...
- 2 篇 工商管理
- 2 篇 公共管理
- 2 篇 图书情报与档案管...
17 篇 理学
- 11 篇 数学
- 8 篇 系统科学
- 4 篇 化学
- 1 篇 物理学
- 1 篇 天文学
- 1 篇 生物学
4 篇 医学
- 3 篇 基础医学(可授医学...
- 1 篇 临床医学
1 篇 文学
- 1 篇 新闻传播学

主题

234 篇 distributed data...
36 篇 data mining
14 篇 grid computing
12 篇 association rule...
10 篇 clustering
10 篇 mapreduce
10 篇 privacy
10 篇 multi-agent syst...
9 篇 parallel data mi...
8 篇 hadoop
7 篇 mobile agent
7 篇 association rule...
7 篇 classification
6 篇 internet of thin...
6 篇 big data
5 篇 parallel algorit...
5 篇 grid
5 篇 knowledge discov...
5 篇 web services
4 篇 distributed clus...

机构

5 篇 univ calabria de...
5 篇 st petersburg el...
5 篇 st petersburg el...
4 篇 univ maryland ba...
4 篇 gdynia maritime ...
3 篇 nanjing univ sta...
3 篇 agnik llc columb...
3 篇 icar cnr i-87036...
2 篇 univ veracruzana...
2 篇 icar cnr arcavac...
2 篇 tsinghua univ de...
2 篇 univ bradford sc...
2 篇 tsinghua univ ts...
2 篇 icar cnr arcavac...
2 篇 univ calabria di...
2 篇 pondicherry univ...
2 篇 islamic azad uni...
2 篇 shanghai jiao to...
2 篇 广西大学
2 篇 ben gurion univ ...

作者

10 篇 talia domenico
9 篇 kholod ivan
8 篇 cesario eugenio
5 篇 czarnowski irene...
5 篇 kargupta h
5 篇 trunfio paolo
4 篇 jedrzejowicz pio...
3 篇 kargupta hillol
3 篇 limon xavier
3 篇 kechadi m-tahar
3 篇 grimaldo francis...
3 篇 le-khac nhien-an
3 篇 giannella c
2 篇 guerra-hernandez...
2 篇 weinberger j
2 篇 di fatta giusepp...
2 篇 fortino giancarl...
2 篇 gyani jayadev
2 篇 rodionov sergey
2 篇 hanley d

语言

229 篇 英文
3 篇 中文
2 篇 其他

检索条件"主题词=distributed data mining"

共 234 条记录，以下是191-200 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Experiments in Multi Agent Learning

Experiments in Multi Agent Learning

引用

3rd International Workshop on Hybrid Artificial Intelligence Systems

作者： Gaya, Maria Cruz Giraldez, J. Ignacio Univ Europea Madrid Madrid 28690 Spain

ISBN: (纸本)9783540876557

data sources are often dispersed geographically in real life applications. Finding a knowledge model may require to join all the data sources and to run a machine learning algorithm on the joint set. We present an alternative based on a Multi Agent System (MAS): an agent mines one data source in order to extract a local theory (knowledge model) and then merges it with the previous MAS theory using a knowledge fusion technique. This way, we obtain a global theory that summarizes the distributed knowledge without spending resources and time in joining data sources. The results show that, as a result of knowledge fusion, the accuracy of initial theories is improved as well as the accuracy of the monolithic solution.

关键词： distributed data mining ensemble techniques MAS evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Rough Set Approach for distributed Decision Tree and Attribute Reduction in the Disseminated Environment

Rough Set Approach for Distributed Decision Tree and Attribu...

引用

International Conference on Advances in Information Technology and Mobile Communication (AIM 2011)

作者： Chandra, E. Ajitha, P. DJ Acad Managerial Excellence Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9783642205729

Attribute reduction is a necessitated step for the disseminated environment in regard to classification and prediction of the data. Traditional approaches were not efficient for optimal attribute reducts. Current techniques are quiet time consuming and less accuracy. Rough set approach is a mathematical technique to handle attribute reducts through data dependencies and structural methods. This paper discusses a novel algorithm for optimal attribute deduct and also increases the accuracy in predicted results and the distributed decision tree classification techniques was made use of to implement the same in the disseminated environment. Proposed algorithm for Construction of distributed decision trees with rough sets increases the accuracy and also reduces the attributes on the time of the massive data sets handling.

关键词： Rough Sets distributed Decision Trees distributed data mining

来源：评论

学校读者我要写书评

暂无评论

Scalability, Memory Issues and Challenges in mining Large data Sets 6

Scalability, Memory Issues and Challenges in Mining Large Da...

引用

2014 International Conference on Intelligent Networking and Collaborative Systems (IEEE INCoS 2014)

作者： Kolici, Vladi Xhafa, Fatos Barolli, Leonard Lala, Algenti Polytech Univ Tirana Tirana Albania Univ Politecn Cataluna Barcelona Spain Fukuoka Inst Technol Fukuoka Japan

ISBN: (纸本)9781479963874

data mining is an active field of research and development aiming to automatically extract "knowledge" from analyzing data sets. Knowledge can be defined in different ways such as discovering (structured, frequent, approximate, etc.) patterns in data, grouping/clustering/bi-clustering data according to one or more criteria, finding association rules, etc. Such knowledge is then fed-back to decision support systems enabling end-users (actors) to make more informed decisions, which in economic terms could lead to advantages as compared to traditional decision support systems. It should be noted however, that data mining algorithms and frameworks have been proposed prior to the "Big data" explosion. While data mining algorithms have considered efficiency and computational complexity as an important requirement, they did not take into account features of Big data such as very large size, velocity with which data is generated, variety, etc. On the other hand, these features are indeed posing issues and challenges to data mining algorithms and frameworks. In this paper we analyse some of the issues in mining large data sets such as scalability and in-memory needs. We also show some computational results pointing out to such issues.

关键词： data mining Large data Sets distributed data mining Hadoop Map Reduce Scalability Memory

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of distributed Association Rule mining Algorithms 7

Performance Evaluation of Distributed Association Rule Minin...

引用

7th International Conference on Communication, Computing and Virtualization (ICCCV)

作者： Sawant, Vinaya Shah, Ketan DJ Sanghvi Coll Engn IT Dept Mumbai Maharashtra India MPSTME IT Dept Mumbai Maharashtra India

Association Rule mining (ARM) is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Most ARM algorithms focus on a sequential or centralized environment where no external communication is required. distributed ARM algorithms (DARM), aim to generate rules from different data sets spread over various geographical sites;hence, they require external communications throughout the entire process. DARM algorithm efficiency is highly dependent on data distribution. The Classical algorithms used in DARM are Count Distribution Algorithm (CDA), Fast distributed mining (FDM) Algorithm and Optimized distributed Association mining (ODAM) Algorithm. This paper presents the implementation details and experimental results of above mentioned algorithms. The paper also highlights the issues of message exchange size in a distributed environment of current DARM algorithms that can affect the communication costs in a distributed environment. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Association Rule mining distributed data mining

来源：评论

学校读者我要写书评

暂无评论

Conditions for Parallel Execution of Functions in data mining Algorithm

Conditions for Parallel Execution of Functions in Data Minin...

引用

IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Kholod, Ivan I. St Petersburg Electrotech Univ LETI Fac Comp Sci & Technol St Petersburg Russia

ISBN: (纸本)9781538643402

The paper describes necessary and sufficient conditions for parallel execution of functions in data mining algorithms. The said conditions take into account data connections between functions based on a variety of usable and modifiable mining model's elements. We determine the conditions for parallel execution in computing environments with distributed and shared memory. As an example, we describe the determination of the conditions for parallel execution of Naive Bayes classifier functions.

关键词： parallel data mining distributed data mining data dependensy

来源：评论

学校读者我要写书评

暂无评论

MapReduce-based H-mine algorithm 5

MapReduce-based H-mine algorithm

引用

Fifth International Conference on Instrumentation & Measurement, Computer, Communication, and Control (IMCCC)

作者： Feng, Xingjie Zhao, Jie Zhang, Zhiyuan CAUC Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781467377232

Frequent Itemset mining (FIM) is a very effective method for knowledge acquisition from data, but with the advent of the era of big data, traditional algorithms based on memory are facing severe challenges such as the computation speed and storage capacity. Fortunately, MapReduce model provides an efficient framework for distributed programming and operation framework. This paper proposes a novel MapReduce-based H-mine algorithm (MRH-mine), a version of H-mine algorithm in the distributed operation environment. Experimental results show that MRH-mine algorithm has a better performance and scalability than traditional H-Mine when facing massive data growth.

关键词： distributed data mining MapReduce H-mine parallelization

来源：评论

学校读者我要写书评

暂无评论

Parallelism and Partitioning in Large-Scale GAs using Spark

Parallelism and Partitioning in Large-Scale GAs using Spark

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Alterkawi, Laila Migliavacca, Matteo Univ Kent Sch Comp Canterbury Kent England Gulf Univ Sci & Technol Management Informat Syst Dept Mubarak Al Abdullah Kuwait

ISBN: (纸本)9781450361118

Big data promises new scientific discovery and economic value. Genetic algorithms (GAs) have proven their flexibility in many application areas and substantial research effort has been dedicated to improving their performance through parallelisation. In contrast with most previous efforts we reject approaches that are based on the centralisation of data in the main memory of a single node or that require remote access to shared/distributed memory. We focus instead on scenarios where data is partitioned across machines. In this partitioned scenario, we explore two parallelisation models: PDMS, inspired by the traditional master-slave model, and PDMD, based on island models;we compare their performance in large-scale classification problems. We implement two distributed versions of Bio-HEL, a popular large-scale single-node GA classifier, using the Spark distributed data processing platform. In contrast to existing GA based on MapReduce, Spark allows a more efficient implementation of parallel GAs thanks to its simple, efficient iterative processing of partitioned datasets. We study the accuracy, efficiency and scalability of the proposed models. Our results show that PDMS provides the same accuracy of traditional BioHEL and exhibit good scalability up to 64 cores, while PDMD provides substantial reduction of execution time at a minor loss of accuracy.

关键词： Genetic Algorithms Big data Spark distributed Learning Classifier System distributed data mining

来源：评论

学校读者我要写书评

暂无评论

data mining Algorithms Parallelization in Logic Programming Framework for Execution in Cluster 19th

Data Mining Algorithms Parallelization in Logic Programming ...

引用

19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems (NEW2AN) / 12th Conference on the Internet of Things and Smart Spaces ruSMART (Are You Smart)

作者： Malov, Aleksey Rodionov, Sergey Shorov, Andrey St Petersburg Electrotech Univ LETI St Petersburg Russia

This article describes an approach to parallelizing of data mining algorithms in logical programming framework, for distributed data processing in cluster. As an example Naive Bayes algorithm implementation in Prolog ... 详细信息

ISBN: (纸本)9783030308599;9783030308582

关键词： Parallel algorithms distributed algorithms data mining distributed data mining MapReduce Logical programming

来源：评论

学校读者我要写书评

暂无评论

A Consensus-Based Approach to the distributed Learning

A Consensus-Based Approach to the Distributed Learning

引用

IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland Gdynia Maritime Univ Chair Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)9781457706530

The paper deals with the distributed learning. distributed learning from data is considered to be an important challenge faced by researchers and practice in the domain of the distributed data mining and distributed knowledge discovery from databases. An effective approach to learning from a geographically distributed data is to select, from the local databases, relevant local patterns, called also prototypes. Such a selection can be based on results of the data reduction process. The paper proposes to carry-out prototype selection at local sites in parallel, independently at each site, employing specialized software agents. To assure obtaining homogenous prototypes at a global level the consensus-based method is proposed and applied. The paper includes a detailed description of the proposed approach and a discussion of the computational experiment results.

关键词： distributed data mining data reduction consensus method

来源：评论

学校读者我要写书评

暂无评论

Framework for Multi Threads Execution of data mining Algorithms

Framework for Multi Threads Execution of Data Mining Algorit...

引用

IEEE North West Russia Section Young Researchers in Electrical and Electronic Engineering Conference (2015 ElConRusNW)

作者： Kholod, Ivan St Petersburg Electrotech Univ LETI Fac Comp Sci & Technol St Petersburg Russia

ISBN: (纸本)9781479973064

the present paper describes the framework for creating data mining algorithms from thread-safe functional blocks. This framework requirements decomposition of algorithms into independently functioning blocks. These blocks must have unified interfaces and implement pure functions. The framework allows create new data mining algorithms from existing blocks and improves the existing algorithms by optimizing single blocks or the whole structure of the algorithms. This becomes possible due to a number of important properties such as thread-safety inherent in pure functions and hence functional blocks.

关键词： data mining parallel data mining data mining algorithms distributed data mining parallel algorithms

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共24页 << < 15 16 17 18 19 20 21 22 23 24 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：