检索结果-内蒙古大学图书馆

Intelligent Hybrid Algorithm for Unsupervised data clustering problem 16th

Intelligent Hybrid Algorithm for Unsupervised Data Clusterin...

16th International Conference on Hybrid Intelligent Systems (HIS) / 8th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Hamdi, Amira Monmarche, Nicolas Slimane, Mohamed Alimi, Adel M. Univ Sfax Res Grp Intelligent Machines REGIM Lab ENIS BP 1173 Sfax 3038 Tunisia Univ Tours Comp Lab LI Polytech Tours Tours France

ISBN: (纸本)9783319529417;9783319529400

Ant based algorithms have proved to be very efficient for solving real problems. These algorithms emphasize flexibility, robustness and decentralized control. Thus more and more researches are interested in this new way of designing intelligent systems in which centralization, control and preprogramming are replaced with self-organization, emergence and autonomy. In this context, many ant based algorithms have been proposed for data clustering problem. The purpose of this paper is to present a new intelligent approach for data clustering problem based on social insect metaphor and FCM algorithm.

关键词： data clustering problem Swarm intelligence Intelligent systems Artificial ants

来源：评论

学校读者我要写书评

暂无评论

A parallel metaheuristic data clustering framework for cloud

引用

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2018年 116卷 39-49页

作者： Tsai, Chun-Wei Liu, Shi-Jui Wang, Yi-Chung Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung Taiwan Taiwan Informat Secur Ctr NCHU TWISC NCHU Taichung Taiwan

A high performance data analytics for internet of things (IoT) has been a promising research subject in recent years because traditional data mining algorithms may not be applicable to big data of IoT. One of the main reasons is that the data that need to be analyzed may exceed the storage size of a single machine. The computation cost of data analysis tasks that is too high for a single computer system is another critical problem we have to confront when analyzing data from an IoT system. That is why an efficient data clustering framework for metaheuristic algorithm on a cloud computing environment is presented in this paper for data analytics, which explains how to divide mining tasks of a mining algorithm into different nodes (i.e., the Map process) and then aggregate the mining results from these nodes (i.e., Reduce process). We further attempted to use the proposed framework to implement data clustering algorithms (e.g., k-means, genetic k-means, and particle swarm optimization) on a standalone system and Spark. The experimental results show that the performance of the proposed framework makes it useful to develop data clustering algorithms on a cloud computing environment. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Metaheuristic algorithm Internet of things data clustering problem

来源：评论

学校读者我要写书评

暂无评论

FlyAntClass: Intelligent move for Ant based clustering Algorithm 13

FlyAntClass: Intelligent move for Ant based Clustering Algor...

引用

13th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)

作者： Hamdi, Amira Slimane, Mohamed Monmarche, Nicolas Alimi, Adel M. Univ Sfax REGIM Lab REs Grp Intelligent Machines ENIS BP 1173 Sfax 3038 Tunisia Univ Tours Polytech Tours Tours France

ISBN: (纸本)9781509043200

This paper provides a new method for arranging data sets into clusters. The proposed model, called FlyAntClass, starts from the ants collective sorting behavior and overwrites it with additional behaviors inspired from birds and spiders: in this context, birds' moving behavior is used to control next relative positions for a moving ant;and spiders' homing behavior is provided to manage movements of ants with conflict situation.

关键词： artificial intelligence swarm intelligence artificial ants data clustering problem LF algorithm

来源：评论

学校读者我要写书评

暂无评论

Spider's behavior for ant based clustering algorithm 15

Spider's behavior for ant based clustering algorithm

引用

15th International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Hamdi, Amira Slimane, Mohamed Monmarche, Nicolas Alimi, Adel M. Univ Sfax ENIS REGIM Lab Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Univ Tours Polytech Tours Tours France

ISBN: (纸本)9781467387095

This paper describes a novel bio-inspired metaheuristic named ASClass for data clustering problem. The particular principles used for the design of this strategy are inspired by the foraging behavior observed in ant colony. In this technique, an ant colony optimization algorithm is used to search a closed tour of minimal length connecting n objects in database. The process of building path-object is inspired by the collective weaving observed in social spiders.

关键词： artificial intelligence swarm intelligence artificial ants data clustering problem ACO algorithms

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：