检索结果-内蒙古大学图书馆

An Analysis of Natural Disaster Data by Using k-Means and k-medoids algorithm of Data Mining Techniques 15

An Analysis of Natural Disaster Data by Using K-Means and K-...

15th International Conference on Quality in Research (QiR) - International Symposium on Electrical and Computer Engineering

作者： Prihandoko Bertalya Ramadhan, Muhammad Iqbal Gunadarma Univ Fac Comp Sci & Informat Technol Depok Jawa Barat Indonesia

ISBN: (纸本)9781509063970

Indonesia is one of the countries with diverse morphology of the lands, high mountains, and the tropical climates of frequent high rainfall. This condition often causes natural disasters in some areas of the country, which sometimes are so terrible that make a lot of people are missing and suffering. In order to reduce the impact of natural disasters to the people and environment, a research was conducted by capturing data showing the occurrence of the disasters and data about the weather conditions for the last five years. Data is obtained from the official sites of Indonesian National Board for Disaster Management (BNPB) and Indonesian Agency for Meteorological, Climatological, and Geophysics (BMkG). This data is then analyzed by using clustering data mining techniques i.e. k-means algorithm and k-medoids algorithm. The two methods are frequently used to make some analysis of data to find some hidden information. The result shows that weather is not the only factor causing natural disaster. By using the result, the government can make some plans for natural disaster mitigations.

关键词： k-means algorithm k-medoids algorithm natural disaster

来源：评论

学校读者我要写书评

暂无评论

Application of the k-medoids Partitioning algorithm for Clustering of Time Series Data 10

Application of the k-medoids Partitioning Algorithm for Clus...

引用

10th IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) - Smart Grids - key Enablers of a Green Power System

作者： Radovanovic, Ana Ye, Xinlin Milanovic, Jovica, V Milosavljevic, Nina Storchi, Riccardo Univ Manchester Dept Elect & Elect Engn Manchester Lancs England Univ Manchester Div Neurosci & Expt Psychol Manchester Lancs England

ISBN: (纸本)9781728171005

Data clustering has been widely applied in numerous areas in order to pave the way for adequate and efficient modelling, control and operation. In the past, most of the data clustering was carried out on static data. However, wider application of time series data has increased the need for time series clustering techniques. This paper presents a comprehensive analysis of the applicability of a standard clustering algorithm, the k-medoids algorithm, for clustering of two diverse time series datasets. The k-medoids algorithm is tested on dynamic power responses of a hybrid renewable energy source plant and neuroscience spike-train data. The main stages in clustering process, that is, data processing, the selection of the optimal distance measure and the estimation of the optimal number of clusters, are analyzed in detail.

关键词： data mining k-medoids algorithm electric power system neuroscience time series clustering

来源：评论

学校读者我要写书评

暂无评论

A Novel k-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering

引用

kNOWLEDGE-BASED SYSTEMS 2019年 175卷 96-106页

作者： Deng, Jiangzhou Guo, Junpeng Wang, Yong Tianjin Univ Coll Management & Econ Tianjin 30072 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Elect Commerce & Logist Chongqing Chongqing 400065 Peoples R China

Data sparsity is a widespread problem of collaborative filtering (CF) recommendation algorithms. However, some common CF methods cannot adequately utilize all user rating information;they are only able to use a small part of the rating data, depending on the co-rated items, which leads to low prediction accuracy. To alleviate this problem, a novel k-medoids clustering recommendation algorithm based on probability distribution for CF is proposed. The proposed scheme makes full use of all rating information based on kullback-Leibler (kL) divergence from the perspective of item rating probability distribution, and distinguishes different items efficiently when selecting the cluster centers. Meanwhile, the distance model breaks the symmetric mode of classic geometric distance methods (such as Euclidean distance) and considers the effects of different rating numbers between items to emphasize their asymmetric relationship. Experimental results on different datasets show that the proposed clustering algorithm outperforms other compared methods in various evaluation metrics;this approach enhances the prediction accuracy and effectively deals with the sparsity problem. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Collaborative filtering Clustering k-medoids algorithm kullback-Leibler (kL) divergence Probability distribution

来源：评论

学校读者我要写书评

暂无评论

Clustered and Thinned Antenna Array for Adaptive Interference Suppression via k-medoids of Desired-Signal Steering Vector

引用

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2024年第3期72卷 2867-2872页

作者： Lee, Yinman Natl Chi Nan Univ Dept Elect Engn Puli 54561 Taiwan

Both clustering and thinning are effective ways to reduce the implementation complexity of large-size antenna arrays. In this communication, we propose simple but efficient approaches to cluster and thin the antenna array for adaptive interference suppression. First, antenna elements are grouped using the $k$ -medoids algorithm according to the knowledge of the desired-signal steering vector. Based on the clustered result, the minimum-variance distortionless response (MVDR) criterion with reduced dimension is then applied to ensure good adaptive-beamforming performance. To lower the implementation complexity and power consumption further, selected antenna elements are turned off on the basis of the perception from clustering. Simulation results show that under suitable settings, the adaptive interference-suppression capability of the proposed clustered and thinned antenna array can be consistently close to that of the optimal MVDR beamformer, and some reasonable compromises between the performance and both the required hardware and computational complexity are achieved.

关键词： Antenna arrays Adaptive arrays Computational complexity Clustering algorithms Signal to noise ratio Hardware Costs Adaptive interference suppression clustered array k-medoids algorithm minimum-variance distortionless response (MVDR) thinned array

来源：评论

学校读者我要写书评

暂无评论

A novel spatial clustering with obstacles constraints based on particle swarm optimization and k-medoids

A novel spatial clustering with obstacles constraints based ...

引用

11th Pacific-Asia Conference on knowledge Discovery and Data Mining

作者： Zhang, Xueping Wang, Jiayao Wu, Mingguang Cheng, Yi Henan Univ Technol Sch Informat Sci & Engn Zhengzhou 450052 Peoples R China PLA Informat Engn Univ Sch Surveying & Mapping Zhengzhou 450052 Peoples R China Liaoning Tech Univ Geomat & Applicat Lab Fuxing 123000 Peoples R China

ISBN: (纸本)9783540717003

In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on PSO and k-medoids, called PkSCOC, which aims to cluster spatial data with obstacles constraints. The PkSCOC algorithm can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering. The results on real datasets show that the PkSCOC algorithm performs better than the IkSCOC algorithm in terms of quantization error.

关键词： spatial clustering particle swarm optimization k-medoids algorithm obstacles constraints

来源：评论

学校读者我要写书评

暂无评论

Genetic k-medoids spatial clustering with obstacles constraints

Genetic K-Medoids spatial clustering with obstacles constrai...

引用

3rd IEEE International Conference on Intelligent Systems

作者： Zhang, Xueping Wang, Jiayao Wu, Fang Fan, Zhongshan Xu, Wenbo Zhengzhou Univ PLA Inst Surveying & Mapping Zhengzhou 450052 Henan Peoples R China PLA Informat Engn Univ Inst Survey & Mapp Zhengzhou Peoples R China Henan Acad Traff Sci & Technol Zhengzhou Peoples R China So Yangtze Univ Sch Informat Technol Wuxi 214122 Jiangsu Peoples R China So Yangtze Univ Sch Informat Technol Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781424401956

Spatial clustering is an important research topic in Spatial Data Mining (SDM). It is not only an important effective method but also a prelude of other task for SDM. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. So, many methods have been proposed in the literature, but few of them have taken into account constraints that may be present in the data or constraints on the clustering. These constraints have significant influence on the results of the clustering process of large spatial data. In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on Genetic algorithms (GAs) and k-medoids, called GkSCOC, which aims to cluster spatial data with obstacles constraints. It can not only give attention to higher local constringency speed and stronger global optimum search, but also consider the obstacles constraints and make the results of spatial clustering more practice. Its performance has compared to GAs, k-medoids;and the results on real datasets show that it is better than standard GAs and k-medoids. The drawback of this method is a comparatively slower speed in spatial clustering.

关键词： clustering genetic algorithms k-medoids algorithm obstacles constraints

来源：评论

学校读者我要写书评

暂无评论

Text Clustering Method Based on k-medoids Social Evolutionary Programming

Text Clustering Method Based on K-medoids Social Evolutionar...

引用

Conference on Electronic Commerce, Web Application and Communication

作者： Hao, ZhanGang Shandong Inst Business & Technol Yantai 264005 Shandong Peoples R China

This article presents a improved social evolutionary programming. The algorithm is the k-medoids algorithm as the main cognitive reasoning algorithm, and improved to learning of Paradigm. Optimal paradigm strengthenin... 详细信息

ISBN: (纸本)9783642286544;9783642286551

关键词： Text clustering k-medoids algorithm social evolutionary programming

来源：评论

学校读者我要写书评

暂无评论

k-center algorithm for hierarchical binary template matching

引用

PATTERN RECOGNITION LETTERS 2019年 125卷 584-590页

作者： Jung, Ho Gi Korea Natl Univ Transportat Dept Elect Engn 50 Daehak Ro Chungju Si 27469 Chungbuk South Korea

To construct a hierarchical template tree of binary templates, the dissimilarity between templates was defined using distance transform, and a k-medoid algorithm was applied to select the representative of a template cluster. However, this method has a limitation in that the representative of a higher level cluster cannot exist in the space between the lower level clusters. In order to solve this problem, this paper proposes a k-center algorithm that finds a template that minimizes the distance to templates belonging to a cluster by using a genetic algorithm regarding each pixel of a binary template as a gene. The search space can be limited by randomly selecting the initial population from the templates belonging to the cluster. In particular, the weighted bidirectional distance is used as the dissimilarity between templates to prevent the cluster center from shrinking. By applying the proposed method to pedestrian silhouettes, it is possible to confirm that a leaf node similar to the given template is better detected and its performance is more stable. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Hierarchical binary template matching k-medoids algorithm Cluster center estimation Weighted bidirectional distance Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Natural gas pipeline valve leakage rate estimation via factor and cluster analysis of acoustic emissions

引用

MEASUREMENT 2018年 125卷 48-55页

作者： Zhu, Shen-Bin Li, Zhen-Lin Zhang, Shi-Min Liang, Le-Le Zhang, Hai-Feng China Univ Petr Coll Mech & Transportat Engn Beijing 102249 Peoples R China PetroChina Pipeline R&D Ctr Langfang 065000 Peoples R China

This paper estimates the leakage rate of a valve in a natural gas pipeline via factor and cluster analysis of acoustic emission signals. Factor analysis was used to reduce the amount of redundant information in the highly dimensional features and extract the optimal features for the cluster analysis. Three types of clustering algorithm-fuzzy C means, k-means and k-medoids-were used to classify leakage rates. Performance was evaluated in terms of overall accuracy, computational time, iterations, Jaccard coefficients and Cohen's kappa. A model based on factor analysis and k-medoids clustering was found to be exceedingly effective for recognizing internal valve leakage rates. This method proved to be superior to the k-means and fuzzy C means clustering methods, and has potential value in real-world applications.

关键词： Valve leakage recognition Acoustic emission k-means algorithm k-medoids algorithm Fuzzy C means algorithm Factor analysis

来源：评论

学校读者我要写书评

暂无评论

Enhanced Neuro-Fuzzy-Based Crop Ontology for Effective Information Retrieval

引用

Computer Systems Science & Engineering 2022年第5期41卷 569-582页

作者： k.Ezhilarasi G.Maria kalavathy Computer sceince and Engineering Anna universityChennai600025India Computer sceince and Engineering St.Joseph’s College of EngineeringChennai600119India

Ontology is the progression of interpreting the conceptions of the information domain for an assembly of *** ontology as information retrieval(IR)aids in augmenting the searching effects of user-required relevant *** crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet,mapping the connection between keywords and information,and categorizing the retrieval *** prevailing procedures for IR consume considerable time,and they could not recover information *** this study,through applying a modified neuro-fuzzy algorithm(MNFA),the IR time is mitigated,and the retrieval accuracy is enhanced for trouncing the above-stated *** proposed method encompasses three phases:i)development of a crop ontology,ii)implementation of the IR system,and iii)processing of user *** the initial phase,a crop ontology is developed and evaluated by gathering crop *** the next phase,a hash tree is constructed using closed frequent patterns(CFPs),and MNFA is used to train the *** the last phase,for a specified user query,CFP is calculated,and similarity assessment results are retrieved using the *** performance of the proposed system is measured and compared with that of existing *** results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.

关键词： Ontology crop ontology information retrieval(IR) k-medoids algorithm neuro-fuzzy algorithm(NFA) modified NFA(MNFA)

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：