检索结果-内蒙古大学图书馆

2nd International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2016

作者： Anil Kumar, K.S. Chacko, Anitha Mary M.O. Sree Ayyappa College Chengannur689109 India

ISBN: (纸本)9781450339629

An intrusion detection system is an intelligent system develope to identify and counteract intrusive efforts. clustering algorithm are used in intrusion detection systems for separating norma activities from abnormal activities. The selection of an efficien clustering technique is a highly challenging job. This articl analyzes the different clustering techniques that can be used in th preprocessing stage of intrusion detection systems. This pape compares the performances of three clustering techniques o DARPA dataset-K-means clustering, K-medoid clustering an Improved K-means clustering technique with optimum cluste centroid initialization algorithm. The best recognition results wit an accuracy of 98.58% was achieved using the Improved K-means algorithm. © 2016 ACM.

关键词： clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

clustering algorithms for area geographical entities in spatial data mining

Clustering algorithms for area geographical entities in spat...

引用

2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Chen, Guang-Xue Li, Xiao-Zhou Chen, Qi-Feng State Key Laboratory of Pulp and Paper Engineering South China University of Technology Guangzhou China Shandong Institute of Light Industry Ji'nan China

ISBN: (纸本)9781424459346

Spatial data mining is the process of identifying or extracting efficient, novel, potentially useful and ultimately understandable patterns from the spatial data set, the spatial clustering analysis is one of the most important research directions in spatial data mining. clustering criterion implied in massive data can be discovered by spatial clustering analysis method which can be used to explore deeper level knowledge combined with other data mining methods and to improve the efficiency and quality of data mining. We studied clustering algorithms of area geographical entities based on geometric shape similarity. And we presented a similarity criterion of line segments shape and a criterion of area geographical entities comprehensively utilizing distance and geometric shape similarity. clustering algorithms based on these criterions are more suitable for clustering analysis of area geographical entities. ©2010 IEEE.

关键词： clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Generating an optimal timetabling for multi-departments common lecturers using hybrid fuzzy and clustering algorithms

引用

SOFT COMPUTING 2019年第13期23卷 4735-4747页

作者： Babaei, Hamed Karimpour, Jaber Hadidi, Amin Islamic Azad Univ Ahar Branch Dept Comp Engn Ahar Iran Univ Tabriz Dept Comp Sci Tabriz Iran Islamic Azad Univ Ahar Branch Dept Mech Engn Ahar Iran

University course timetabling is a NP-hard problem that be performed for each semester frequently. In this paper, we use a two-step algorithm for timetabling of common lecturers among departments. In the first step, we use a fuzzy multi-criteria decision-making comparison and local search algorithms with seven neighborhood structures and random iteration. It means that we use a fuzzy multi-criteria comparison algorithm to eliminate the ambiguities and soft constraints of common lecturers among departments. In addition, we apply the local search algorithm with seven neighboring structures to avoidtrapping intolocaloptima and improve the fuzzy multi-criteria comparison over the preferences and soft constraints of lecturers. In the second step, the common lecturers' timetable generated in the first step by the clustering approach (k-means, fuzzy c-means and funnel shape) is clustered based on the preferences and soft constraints of common lecturers among departments. Now, our common lecturers prepared by the clustering algorithms are mapped to the traversed free resources according to the paper's aims: (1) descending satisfaction of preferences and soft constraints of common lecturers among departments and (2) minimizing the loss of extra resources of each faculty, so that an optimal instance of our common lecturers timetabling is generated among departments. The applied datasets are in terms of satisfying the scheduling requirements in the real world for multi-departments of Islamic Azad University of Ahar branch.

关键词： clustering algorithms Common lecturers Fuzzy multi-criteria decision-making approach Local search University courses timetabling

来源：评论

学校读者我要写书评

暂无评论

Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

引用

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第6期31卷 p1-28页

作者： Memari, Mehran Karimi, Ali Hashemi-Dezaki, Hamed Univ Kashan Fac Elect & Comp Engn 6 Km Ghotbravandi Blvd Kashan *** Iran Univ West Bohemia UWB Fac Elect Engn Reg Innovat Ctr Elect Engn RICE Plzen Czech Republic

The reliability of the smart grid is adversely affected due to system uncertainties. Also, the steadily growing deployment of renewable distributed generation (DG) units increases the uncertainties of smart grids. Hence, it is essential to concern the uncertainties in the field of reliability evaluation of smart grids. Although the Monte Carlo simulation (MCS) has received a significant deal of consideration in the literature, there is a research gap in using the clustering algorithms to assess smart grids' reliability. This article aims to fill such a research gap by proposing a new reliability assessment method, using various clustering algorithms. The benefits from the proposed method's accuracy and fast computation are highlighted, while optimal operation, optimal short-term planning, and repetitive problems should be studied. In this paper, the performance and accuracy of various classic (k-means, fuzzy c-means, and k-medoids) and metaheuristic (genetic algorithm, particle swarm optimization, differential evolutionary, harmony search, and artificial bee colony) clustering algorithms are studied. Comparing different scenario reduction algorithms in the proposed reliability evaluation method is one of the most contributions. The proposed method is applied to two realistic test systems. Test results infer that the proposed method is adequately precise, while the required computation time is less than MCS-based approaches. Test results for both test systems imply that the accurate expected energy not supplied (EENS) with less than 2.1% is achievable applying the proposed method. The fuzzy c-means clustering algorithm results in the best accuracy among the studied classic and nonclassic (metaheuristic) algorithms.

关键词： clustering algorithms Monte Carlo simulation reliability evaluation renewable distributed generations scenario reduction smart grids

来源：评论

学校读者我要写书评

暂无评论

Self-Organizing Map and clustering algorithms for the analysis of occupational accident databases

引用

SAFETY SCIENCE 2011年第8-9期49卷 1215-1230页

作者： Palamara, Federica Piglione, Federico Piccinini, Norberto Politecn Torino Dept Elect Engn I-10129 Turin Italy Politecn Torino Dept Struct & Geotech Engn I-10129 Turin Italy Politecn Torino Dept Mat Sci & Chem Engn I-10129 Turin Italy

Data mining techniques are a powerful method for extracting information from large databases. Among these techniques, clustering and projection of data from high-dimensional spaces hold a main role, since they allow to discover hidden structures in the data set. Following this approach, this paper presents a data analysis method designed to help the management and investigation of occupational accident databases. The purpose is to discover the most common sequences of events leading to accidents for devising preventive actions. To this aim, we developed a two-level approach based on the joint use of the Kohonen's Self-Organizing Map and the k-means clustering algorithm. This approach allows not only to group the accidents in different classes but also to visualize them in a way understandable for the analyst. The method has been applied with satisfactory results to a large database of occupational accidents occurred in the Italian wood processing industry. A comparison with the Hierarchical clustering method confirmed the effectiveness of the proposed approach. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Occupational accidents Wood industry Neural networks Self-Organizing Map clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

An In-Depth Analysis of COVID-19 Symptoms Considering the Co-Occurrence of Symptoms Using clustering algorithms

引用

IEEE ACCESS 2024年 12卷 127792-127804页

作者： Benito, Diego Javier Robles, Jesus Rufino Ramirez, Juan Anta, Antonio Fernandez Aguilar, Jose IMDEA Networks Inst Leganes 28918 Madrid Spain Continental AG D-30175 Hannover Germany Univ Los Andes Ctr Estudios Microelect & Sistemas Distribuidos CE Merida 5115 Venezuela Univ EAFIT GIDITIC Medellin 050021 Colombia

A comprehensive analysis of the COVID-19 pandemic is necessary to prepare for future healthcare challenges. In this regard, the large number of datasets collected during the pandemic has allowed various studies on disease behavior and characteristics. For example, collected datasets can be used to extract knowledge about the symptomatic behavior of the disease. In this work, we are interested in analyzing the relationships between the different symptoms of the disease, considering various dimensions, such as countries, variants of COVID-19, and age groups. To this end, we consider the co-occurrence of symptoms as a fundamental element. More precisely, we implemented clustering techniques to discover symptomatic patterns across the various dimensions. For instance, in analyzing the dominant patterns, we observe that symptom congestion or runny nose almost always appears with the symptom muscle pain across many dimensions. Hence, the information on symptom patterns can be helpful in decision-making processes to detect and combat COVID-19 and similar diseases.

关键词： COVID-19 Indexes Pandemics Surveys Social networking (online) Pain Medical diagnostic imaging COVID-19 symptoms symptomatic patterns machine learning clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Efficient Distributed clustering algorithms on Star-Schema Heterogeneous Graphs

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第10期34卷 4781-4796页

作者： Chen, Lu Gao, Yunjun Huang, Xingrui Jensen, Christian S. Zheng, Bolong Zhejiang Univ Coll Comp Sci Hangzhou 310027 Zhejiang Peoples R China Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China

Many datasets including social media data and bibliographic data can be modeled as graphs. clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals.

关键词： clustering algorithms Computational modeling Distributed databases clustering methods Blogs Social networking (online) Scalability Heterogeneous graph clustering distributed processing algorithm

来源：评论

学校读者我要写书评

暂无评论

Directed Graph clustering algorithms, Topology, and Weak Links

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第6期52卷 3995-4009页

作者： Zhang, Xiao Lian, Bosen Lewis, Frank L. Wan, Yan Cheng, Daizhan Chinese Acad Sci Acad Math & Syst Sci Natl Ctr Math & Interdisciplinary Sci Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Texas Arlington UTA Res Inst Ft Worth TX 76118 USA Univ Texas Arlington Elect Engn Dept Arlington TX 76019 USA Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

In this article, a general approach for directed graph clustering and two new density-based clustering objectives are presented. First, using an equivalence between the clustering objective functions and a trace maximization expression, the directed graph clustering objectives are converted into the corresponding weighted kernel k-means problems. Then, a nonspectral algorithm, which covers both the direction and weight information of the directed graphs, is thus proposed. Next, with Rayleigh's quotient, the upper and lower bounds of clustering objectives are obtained. After that, we introduce a new definition of weak links to characterize the effectiveness of clustering. Finally, illustrative examples are given to demonstrate effectiveness of the results. This article provides a glance at the potential connection between density-based and pattern-based clustering. Compared with other approaches for directed graph clustering, the method proposed in this article naturally avoids the loss of the nonsymmetric edge data because there is no need for any additional symmetrization.

关键词： Directed graphs clustering algorithms Symmetric matrices Topology Kernel Network topology Linear programming Directed graph clustering nonspectral algorithm Rayleigh's quotient weighted kernel k-means

来源：评论

学校读者我要写书评

暂无评论

Hamiltonian-Based clustering algorithms FOR STATIC AND DYNAMIC clustering IN DATA MINING AND IMAGE PROCESSING

引用

IEEE CONTROL SYSTEMS MAGAZINE 2012年第4期32卷 74-91页

作者： Casagrande, Daniele Sassano, Mario Astolfi, Alessandro Univ Udine Dept Elect Managerial & Mech Engn I-33100 Udine Italy Imperial Coll London Dept Elect & Electron Engn London England

The large amount of data available for analysis and management raises the need for defining, determining, and extracting meaningful information from the data. Hence in scientific, engineering, and economics studies, the practice of clustering data arises naturally when sets of data have to be divided into subgroups with the aim of possibly deducting common features for data belonging to the same subgroup. For instance, the innovation scoreboard [1] (see Figure 1) allows for the classification of the countries into four main clusters corresponding to the level of innovation defining the “leaders,” the “followers,” the “trailing,” and the “catching up” countries. Many other disciplines may require or take advantage of a clustering of data, from market research [2] to gene expression analysis [3], from biology to image processing [4][7]. Therefore, several clustering techniques have been developed (for details see “Review of clustering algorithms”).

关键词： clustering algorithms Technological innovation Feature extraction Marketresearch Data mining Gene expressoin

来源：评论

学校读者我要写书评

暂无评论

(k, r)-Dominating set-based, weighted and adaptive clustering algorithms for mobile ad hoc networks

引用

IET COMMUNICATIONS 2011年第13期5卷 1836-1853页

作者： Anitha, V. S. Sebastian, M. P. Govt Engn Coll Dept Comp Sci & Engn Wayanad Kerala India Indian Inst Management Calicut Kerala India

The creation of stable, scalable and adaptive clusters with good performance, faster convergence rate and with minimal overhead is a challenging task in mobile ad hoc networks (MANETs). This study proposes two clustering techniques for MANETs, which are (k, r)-dominating set-based, weighted and adaptive to changes in the network topology. The set of dominating nodes functions as the clusterheads. The scenario-based clustering algorithm for MANETs (SCAM) is a greedy approximation algorithm, whereas the distributed-SCAM (DSCAM) selects the (k, r)-dominating set through a distributed election mechanism. These algorithms achieve variable degree of clusterhead redundancy through the parameter k which contributes to reliability. Similarly, flexibility in creating variable diameter clusters is achieved with the parameter r. To improve the stability of the created clusters, the affiliation of other nodes with the clusterhead is decided based on the quality of the clusterhead, which is a function of connectivity, stability, residual battery power and transmission range. Mechanisms are available for accounting the group mobility and load balancing. The performance of these algorithms are evaluated through simulation and the results show that these algorithms create stable, scalable and load-balanced clusters with relatively less control overhead in comparison with the existing popular algorithms.

关键词： Mobile ad hoc networks Load balancing Battery power supply Approximation algorithms Network topology algorithms clustering algorithms Self tuning

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：