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
- 5 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
36 篇 管理学
- 32 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...
19 篇 理学
- 13 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161-170 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：

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

Trace mining from distributed Assembly databases for Causal Analysis

Trace Mining from Distributed Assembly Databases for Causal ...

引用

13th Pacific-Asia Conference on Knowledge Discovery and data mining

作者： Hido, Shohei Matsuzawa, Hirofumi Kitayama, Fumihiko Numao, Masayuki IBM Res Corp Tokyo Res Lab Tokyo Japan IBM Japan Ltd Global Business Serv Tokyo Japan Univ Electrocommun Dept Comp Sci Tokyo Japan

ISBN: (纸本)9783642013065

Hierarchical structures of components often appear in industry, such as the components of cars. We focus on association mining from the hierarchically assembled data items that are characterized with identity labels such as lot numbers. Massive and physically distributed product databases make it difficult to directly find the associations of deep-level items. We propose a top-down algorithm using virtual lot, numbers to mine association rules from the hierarchical databases. Virtual lot numbers delegate the identity information of the subcomponents to upper-level lot numbers without modifications to the databases. Our pruning method reduces the number of enumerated items and avoids redundant access to the databases. Experiments show that the algorithm works an order of magnitude faster than a naive approach.

关键词： distributed data mining causal analysis product recall

来源：评论

学校读者我要写书评

暂无评论

Dimension Reduction Methods for Collaborative Mobile Gossip Learning 24

Dimension Reduction Methods for Collaborative Mobile Gossip ...

引用

24th Euromicro International Conference on Parallel, distributed, and Network-Based Processing (PDP)

作者： Berta, Arpad Hegedus, Istvan Jelasity, Mark Univ Szeged Szeged Hungary Univ Szeged MTA SZTE Res Grp Al Szeged Hungary

ISBN: (纸本)9781467387767

Decentralized learning algorithms are very sensitive to the size of the raw data records due to the resulting large communication cost. This can, in the worst case, even make decentralized learning infeasible. Dimension reduction is a key technique to compress data and to obtain small models. In this paper, we propose a number of robust and efficient decentralized approaches to dimension reduction in the system model where each network node holds only one data record. These algorithms build on searching for good random projections. We present a thorough experimental comparison of the proposed algorithms and compare them with a variant of distributed singular value decomposition (SVD), a state-of-the-art algorithm for dimension reduction. We base our experiments on a trace of real mobile phone usage. We conclude that our method based on selecting good random projections is preferable and provides good quality results when the output is required on a very short timescale, within tens of minutes. We also present a hybrid method that combines the advantages of random projections and SVD. We demonstrate that the hybrid method offers good performance over all timescales.

关键词： distributed data mining gossip dimension reduction

来源：评论

学校读者我要写书评

暂无评论

Some Observations of Sequential, Parallel and distributed Association Rule mining Algorithms

Some Observations of Sequential, Parallel and Distributed As...

引用

International Conference on Computer and Automation Engineering

作者： Garg, Rakhi Mishra, P. K. Banaras Hindu Univ Dept Comp Sci Varanasi 221005 Uttar Pradesh India

ISBN: (纸本)9780769535692

This paper mainly focuses on the need, merits, demerits and designing of different Sequential, Parallel and distributed ARM algorithms developed so far on different hardware platforms and categorising them according to database format used, search techniques used and whether they find all or maximal frequent itemsets. The goal of this survey is to provide information that serve as a reference for both the researchers and practitioners interested in the designing and implementation of ARM algorithms that has high performance in terms of response time and the ability to scale massive data sets with minimum communication overheads, privacy and security, which is the requirement of today's world.

关键词： parallel data mining distributed data mining association rule mining

来源：评论

学校读者我要写书评

暂无评论

mining Fuzzy Association Rules Using MapReduce Technique

Mining Fuzzy Association Rules Using MapReduce Technique

引用

International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Gabroveanu, Mihai Cosulschi, Mirel Slabu, Florin Univ Craiova Dept Comp Sci Craiova Romania

ISBN: (纸本)9781467399104

mining association rules from large databases is one of the most important tasks from data mining. Nowadays, the majority of companies produce a significant amount of data stored in distributed databases. In this case, most of the traditional algorithms for mining association rules become ineffective because they require a lot of resources to extract the frequent patterns. The cloud computing technologies provide us the infrastructure for handling such massive datasets. In this paper, we propose an extension of the Count Distribution algorithm for mining fuzzy association rules from a distributed database. The algorithm uses the MapReduce programming model, which aims to distribute the mining process over many cluster nodes. Distributing the mining process allows handling very large databases and significantly improves the execution time.

关键词： distributed data mining fuzzy association rules MapReduce

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training 1

引用

27th International Conference on database Systems for Advanced Applications (DASFAA)

作者： Zeng, Shenglai Li, Zonghang Yu, Hongfang He, Yihong Xu, Zenglin Niyato, Dusit Yu, Han Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (数字)9783031001260

ISBN: (纸本)9783031001260;9783031001253

Federated learning (FL) is a rapidly growing privacy preserving collaborative machine learning paradigm. In practical FL applications, local data from each data silo reflect local usage patterns. Therefore, there exists heterogeneity of data distributions among data owners (a.k.a. FL clients). If not handled properly, this can lead to model performance degradation. This challenge has inspired the research field of heterogeneous federated learning, which currently remains open. In this paper, we propose a data heterogeneity-robust FL approach, FEDGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training. FEDGSP assigns FL clients to homogeneous groups to minimize the overall distribution divergence among groups, and increases the degree of parallelism by reassigning more groups in each round. It is also incorporated with a novel Inter-Cluster Grouping (ICG) algorithm to assist in group assignment, which uses the centroid equivalence theorem to simplify the NP-hard grouping problem to make it solvable. Extensive experiments have been conducted on the non-i.i.d. FEMNIST dataset. The results show that FEDGSP improves the accuracy by 3.7% on average compared with seven state-of-the-art approaches, and reduces the training time and communication overhead by more than 90%.

关键词： Federated learning distributed data mining Heterogeneous data Clustering-based learning

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Naive Bayes Classification 8th

Privacy-Preserving Naive Bayes Classification

引用

8th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Huai, Mengdi Huang, Liusheng Yang, Wei Li, Lu Qi, Mingyu Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China Univ Sci & Technol China Suzhou Inst Adv Study Suzhou 215123 Peoples R China

ISBN: (纸本)9783319251592;9783319251585

In this paper, we propose differentially private protocols for Naive Bayes classification over distributed data. Compared with existing works, the privacy and security models in the proposed protocols are stronger: firstly, both the miner and parties can be arbitrarily malicious and can collude with each other to violate the remaining honest parties privacy;secondly, all communication channels between them can be assumed to be insecure. Specifically, we build a guarantee of differential privacy into the cryptographic construction so that the proposed protocols can tolerate collusions and resist eavesdropping attacks which are caused by insecure communication channels. Additionally, the proposed protocols can be implemented at lower computation and communication costs, and some extensions to our protocols (e.g. supporting parties dynamic joins or leaves) are also proposed in this paper. Both theoretical analysis and simulation results show that the proposed privacy-preserving protocols for Naive Bayes have strong security and better classification performance than the standard one.

关键词： distributed data mining Naive Bayes Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Secure Cloud mining

Secure Cloud Mining

引用

3rd IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

作者： Ansari, Ahlam Shakeel Ahmed Devadkar, Kailas Kisan Sardar Patel Inst Technol Dept Comp Engn Bombay Maharashtra India Sardar Patel Inst Technol Dept Informat Technol Bombay Maharashtra India

ISBN: (纸本)9781467324816;9781467313445

Cloud computing is a solution for cheap and efficient storage;it is also used for analyzing mass data. Its aim is to power up the next generation data centers. Many companies are opting for cloud storage;hence it is important to use an efficient and effective data mining strategy to mine the cloud storage to extract interesting patterns and relationship between variables in large databases. These data patterns are nothing but forecasting or predictions that can be used by the companies in near future to increase their sales. But the predictions that are generated as a result of mining should be secure from interception. Hence we propose a Secure Cloud mining (SCM) architecture that will generate a Secure Forecasting Report (SFR) for the company.

关键词： Cloud mining Security Cloud Storage distributed data mining

来源：评论

学校读者我要写书评

暂无评论

Dependability in data mining: A perspective from the cost of making decisions

Dependability in data mining: A perspective from the cost of...

引用

1st International Conference on Availability, Reliability and Security

作者： Chung, H. Michael Calif State Univ Long Beach CIST Long Beach CA 90840 USA

ISBN: (纸本)0769525679

This study describes the cost involved in maintaining dependability that involves availability, reliability and security. The comparison of the cost as well as the need to get additional data set to improve the predictive accuracy is fundamentally an economic and policy decision. It is also highly dependent on the nature of the task. For the tasks that deal with massive data sets and/or the tasks often require real time decision making are the ones that would benefit from the cost perspective.

关键词： data mining dependability error type decision making cost distributed data mining

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of a distributed Clustering Approach for Spatial datasets 15th

Performance Evaluation of a Distributed Clustering Approach ...

引用

15th Australasian data mining Conference (AusDM)

作者： Bendechache, Malika Nhien-An Le-Khac Kechadi, M-Tahar Univ Coll Dublin Insight Ctr Data Analyt Obrien BldgCtr East Dublin 04 Ireland Univ Coll Dublin Dublin 04 Ireland

ISBN: (纸本)9789811302923;9789811302916

The analysis of big data requires powerful, scalable, and accurate data analytics techniques that the traditional data mining and machine learning do not have as a whole. Therefore, new data analytics frameworks are needed to deal with the big data challenges such as volumes, velocity, veracity, variety of the data. distributed data mining constitutes a promising approach for big data sets, as they are usually produced in distributed locations, and processing them on their local sites will reduce significantly the response times, communications, etc. In this paper, we propose to study the performance of a distributed clustering, called Dynamic distributed Clustering (DDC). DDC has the ability to remotely generate clusters and then aggregate them using an efficient aggregation algorithm. The technique is developed for spatial datasets. We evaluated the DDC using two types of communications (synchronous and asynchronous), and tested using various load distributions. The experimental results show that the approach has superlinear speed-up, scales up very well, and can take advantage of the recent programming models, such as MapReduce model, as its results are not affected by the types of communications.

关键词： distributed data mining distributed computing Synchronous communication Asynchronous communication Spacial data mining Super-speedup

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 << < 13 14 15 16 17 18 19 20 21 22 > >>

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：