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检索条件"主题词=clustering algorithms"

共 51242 条记录，以下是341-350 订阅

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A Comparison of HEED Based clustering algorithms -- Introducing ER-HEED

A Comparison of HEED Based Clustering Algorithms -- Introduc...

引用

International Conference on Advanced Information Networking and Applications (AINA)

作者： Zaib Ullah Leonardo Mostarda Roberto Gagliardi Diletta Cacciagrano Flavio Corradini Computer Science Division University of Camerino Italy

ISBN: (纸本)9781509018598

A Wireless Sensor Network (WSN) is composed of distributed sensors with limited processing capabilities and energy restrictions. These unique attributes pose new challenges amongst which prolonging the WSN lifetime is one of the most important. clustering is an energy efficient routing technique that has been widely applied to report data from the WSN nodes to a centralised Base Station. A plethora of different clustering protocols have been proposed. Some protocols are based on equal-sized clusters while others use clusters of unequal size. Some others make use of rotation techniques to reduce the amount of cluster head elections. When different clustering approaches are presented different simulation settings are used. In this paper we perform a comparison study of HEED based clustering protocols that are HEED, UHEED, RUHEED and a novel variation of R-HEED that is ER-HEED. We have considered the same network model, the same energy consumption model and we have compared the lifetime of the protocols by considering various case studies. Our comparison study shows that the selection of the protocol to be used depends on the case study and the WSN lifetime measure that is considered.

关键词： Sensors Protocols Nominations and elections Wireless sensor networks clustering algorithms Base stations Relays

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Review of Web Document clustering algorithms

Review of Web Document Clustering algorithms

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sanjib Kumar Sahu Shalini Srivastava Dept. of Computer Science Utkal University Bhubaneswar Odisha INDIA University School of Information & Communication Technology GGSIP University Delhi INDIA

ISBN: (纸本)9781467394178

Users of search engines are fond of accurate and fast results. Web is now overloaded with lots of pages or documents dealing with same topic. Thus they are often forced to surf through the large and irrelevant set of results. This has forced the IR community to explore such document clustering techniques capable of providing fast and accurate results. Even after being a very effective solution, clustering is yet not deployed on the major search engines. This paper will articulate the requirements of Web Document clustering and reports on the clustering methods belonging in this domain. The focus of ours is on; these methods create their clusters based on the characters or individual terms rather than showcasing them as a single phrase with a meaning and sequence of words. Paper will be reporting general term based and phrase based techniques and will provide conclusion based on their individual efficiency to work with their key methods.

关键词： clustering algorithms Search engines Indexes Matrix decomposition Web pages Algorithm design and analysis Conferences

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Comparison of selected clustering algorithms of raw data obtained by interferometric methods using artificial neural networks

Comparison of selected clustering algorithms of raw data obt...

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International Radar Symposium (IRS)

作者： Marta Wlodarczyk-Sielicka Jacek Lubczonek Andrzej Stateczny Institute of Geoinformatics Maritime University of Szczecin Szczecin Poland Marine Technology Ltd. Szczecin Poland

ISBN: (纸本)9781509025190

The article presents a particular comparison of selected clustering algorithms of data obtained by interferometrie methods using artificial neural networks. For the purposes of the experiment original data from Szczecin Port have been tested. For collecting data authors used the interferometric sonar system GeoSwath Plus 250 kHz. GeoSwath Plus offers very efficient simultaneous swath bathymetry and side scan seabed mapping. During the use of Kohonen's algorithm, the network, during learning, use the Winner Take All rule and Winner Take Most rule. The parameters of the tested algorithms were maintained at the level of default. During the research several populations were generated with number of clusters equal 9 for data gathered from the area of 100m 2 . In the subsequent step statistics were calculated and outcomes were shown as spatial visualization and in tabular form.

关键词： clustering algorithms Navigation Neurons Artificial neural networks Algorithm design and analysis Ports (Computers) Standards

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On Possibilistic clustering algorithms Based on Noise clustering

On Possibilistic Clustering Algorithms Based on Noise Cluste...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Yuchi Kanzawa Department of Communications Engineering Shibaura Institute of Technology Tokyo JAPAN

ISBN: (纸本)9781509026791

In this study, several possibilistic clustering methods are proposed based on noise clustering. The proposed methods are motivated by the fact that conventional possibilistic clustering methods do not correspond with noise clustering methods in entropy-regularized situations, whereas these methods do correspond in Bezdek-type fuzzified situation.

关键词： clustering methods Optimization Entropy clustering algorithms Mixture models Intelligent systems Expectation-maximization algorithms

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IEEE International Conference on Semantic Computing

作者： Chantal Fry Sukanya Manna Computer Science Department California State Polytechnic University

ISBN: (纸本)9781509006632

The amount of unstructured text data available is growing exponentially due to the proliferation of digital information such as emails, text messages, blogs, social media posts, and product reviews. For users of e-commerce websites such as Amazon, navigating thousands of reviews before buying a product can be a daunting task. Unsupervised machine learning techniques can be used to automatically analyze preprocessed data from these websites in order to provide consumers with an improved user experience before purchasing a product. In this work, we leverage two flat clustering algorithms on Amazon review data: K-means and Peak-searching to perform clustering of product reviews based on topic. The experimental results show that K-means clustering performs better than Peak-searching clustering in terms of grouping similar reviews based on topics.

关键词： clustering algorithms websites Case studies Consumers digital information electronic commerce Purchasing Cluster Analysis

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Fuzzy clustering algorithms — Review of the Applications

Fuzzy Clustering Algorithms — Review of the Applications

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Jiamin Li Harold W. Lewis Department of Systems Science and Industrial Engineering The State University of New York at Binghamton Binghamton NY USA

ISBN: (纸本)9781509052646

Fuzzy clustering is an alternative method to conventional or hard clustering algorithms, which makes partitions of data containing similar subjects. The tendency of adopting machine learning, big data science, cloud computation in various industries depends on unsupervised learning on data structures to tell the story about consumers' behavior, fraud detection, and market segmentation. Fuzzy clustering contrasts with hard clustering by its nonlinear nature and discipline of flexibility in grouping massive data. It provides more accurate and close-to-nature solutions for partitions and herein implies more possibility of solutions for decision-making. In the specific matter of computation, fuzzy clustering has its roots in fuzzy logic and indicates the likelihood or degrees of one data point belonging to more than one group. This paper focuses on the study of models of fuzzy clustering in various cases. Uniquely designed algorithms enhance the accuracy of outcomes and are worth studying to assist future work. In some case scenarios, modeling processes are data-driven and place emphasis on the distances between points and new centers of clusters. In some other cases, which aim at market segmentation or evaluation of patients by healthcare records, membership degree is a key element in the algorithm. This paper surveys a wide-range of research that has well-designed mathematic models for fuzzy clustering, some of which include genetic algorithms and neural networks. The last section introduces open sources of Python and displays sample results from hands-on practice with these packages.

关键词： clustering algorithms Indexes Genetic algorithms Euclidean distance Data structures Mathematical model Histograms

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Classical and Re-learning Based clustering algorithms for Huge Data Warehouses

Classical and Re-learning Based Clustering Algorithms for Hu...

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IEEE International Conference on Computational Intelligence & Communication Technology (CICT)

作者： Syed Zubair Ahmad Shah Mohammad Amjad Department of Computer Engineering Jamia Millia Islamia New Delhi

The aim of writing this paper is to provide a detailed, in order description and analysis of the often used and important algorithms of clustering with focus on the recent advances, and to provide an extensive compari... 详细信息

ISBN: (纸本)9781509002115

关键词： clustering algorithms Partitioning algorithms Couplings Complexity theory Feature extraction Data warehouses Algorithm design and analysis

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Perceptron-based AOW clustering algorithms

Perceptron-based AOW Clustering Algorithms

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Huozhu Wang Jianguo Yu Beijing University of Posts and Telecommunications

ISBN: (纸本)9781510823808

clustering algorithms has been widely used in Ad hoc network with its ability to construct network quickly,conveniently and flexibly and without the need of default network *** this paper,Firstly,some shortcomings of the typical algorithm AOW(adaptive on-demand weighted algorithm) are introduced and ***,we discusses the calculating method of nodes weights with perceptron algorithm and the cluster heads selecting process with modified algorithms based on original AOW to meet system ***,a adaptive on-demand weighted algorithm based perceptron( Per AOW) is proposed to select cluster heads in Ad hoc *** to AOW,simulation results proved that the proposed algorithm are improving network topological structure and giving 5.2% better load balance factor( LBF).

关键词： clustering algorithms Ad hoc network adaptive on-demand weighted algorithm

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The Best clustering algorithms in Data Mining

The Best Clustering Algorithms in Data Mining

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International Conference on Communication and Signal Processing

作者： K. M. Archana Patel Prateek Thakral Computer Application Department NIT Kurukshetra Haryana India Computer application Department NIT Kurukshetra Haryana India

ISBN: (纸本)9781467385497

In data mining, clustering is the most popular, powerful and commonly used unsupervised learning technique. It is a way of locating similar data objects into clusters based on some similarity. clustering algorithms can be categorized into seven groups, namely Hierarchical clustering algorithm, Density-based clustering algorithm, Partitioning clustering algorithm, Graph-based algorithm, Grid-based algorithm, Model-based clustering algorithm and Combinational clustering algorithm. These clustering algorithms give different result according to the conditions. Some clustering techniques are better for large data set and some gives good result for finding cluster with arbitrary shapes. This paper is planned to learn and relates various data mining clustering algorithms. algorithms which are under exploration as follows: K-Means algorithm, K-Medoids, Distributed K-Means clustering algorithm, Hierarchical clustering algorithm, Grid-based Algorithm and Density based clustering algorithm. This paper compared all these clustering algorithms according to the many factors. After comparison of these clustering algorithms I describe that which clustering algorithms should be used in different conditions for getting the best result.

关键词： Cluster size clustering algorithms Data normalization Time complexity of algorithm

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Hierarchical clustering algorithms for heterogeneous energy harvesting wireless sensor networks

Hierarchical clustering algorithms for heterogeneous energy ...

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International Symposium on Wireless Communication Systems (ISWCS)

作者： Sadia Waheed Awan Sajid Saleem School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST) Islamabad Pakistan

WSNs are achieving significance importance in the information and communication technologies with the development of Internet-of-Things (IoT). WSNs are connected with the Internet in many industrial applications. However, energy efficiency is one of the challenging problems in wireless sensor networks (WSNs). In this paper, an optimized location of cluster heads (CHs) for energy efficiency in a heterogeneous WSN (that consists of few energy harvesting sensors and mostly non-renewable sensors) is studied. LEACH is an effective method for clustering in a homogeneous WSN, consisting solely of non-renewable sensors with uniform energy profile. Our proposed clustering schemes suggest simple and static clustering strategy for a heterogeneous WSN with good harvesting efficiency. The proposed schemes divide the total network covered area into cells, based on different criterion. In regular grid (RG) approach, a CH is simply placed in the center of each cell. Minimax grid (MG) approach attempts to improve the lifetime of the network by placing the CH at the center of the smallest enclosing circle. K-Medoids (KM) approach first divides the network into clusters and then solves a facility location problem to assign the role of CHs to EH sensors. Simulation results show that RG, MG and KM perform better than LEACH in terms of energy consumption, and consequently, increase the lifetime of the network by upto 200% when the harvested energy is available in healthy amounts. The relative improvement of KM and MG over RG is also discussed in this paper.

关键词： wireless sensor networks energy conservation energy consumption energy harvesting minimax techniques pattern clustering telecommunication power management Wireless sensor networks energy harvesting minimax techniques clustering algorithms Cluster Analysis energy consumption Energy conservation leaching clustering methods REGULATORY GUIDE Magnesium MOTOR GENERATOR Energy efficiency WSN gene

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