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International Conference on Communication Systems

作者： Dahlila Putri Dahnil Yashwant Prasad Singh Chin Kuan Ho Faculty of Information Technology Multimedia University Cyberjaya Selangor Darul Ehsan Malaysia

The paper presents the criteria that affect the performance of network lifetime in Wireless Sensor Networks. In sensor networks the nodes can be organized into clusters for one hop energy-efficient data routing. This work focuses on adaptive clustering of sensor nodes where the role of cluster head changes among nodes. The nodes take turn to become cluster head based on one or several parameters such as random selection, nodes residual energy, nodes ID, node proximity to other nodes or nodes with different energy levels (advanced nodes). Ideally, the number of elected cluster heads must match the optimal number of cluster heads which is determined a priori or via simulation for energy-efficient routing. Achieving the optimal number of cluster heads is crucial as this will ensure the minimum energy consumption for data transmission to base station. This paper presents simulation results of existing adaptive clustering algorithms. The simulation results show how the election criteria for cluster heads election such as node residual energy, node proximity to other nodes, random election and nodes with different energy level affect the number of cluster heads elected, their distribution in the network and the network lifetime. Simulation results are provided to show the comparative effectiveness of different clustering algorithm on network lifetime, cluster head selection and their distribution in the network. Modifications on the cluster head election criteria are done that combined the node residual energy and nodes proximity to other nodes and simulation shows that the network lifetime is improved.

关键词： Protocols Nominations and elections Wireless sensor networks Simulation Base stations clustering algorithms Energy states

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Analysis of epileptic magnetoencephalogram dynamics through clustering algorithms

Analysis of epileptic magnetoencephalogram dynamics through ...

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International Conference on Digital Signal Processing (DSP)

作者： A.V. Adamopoulos B. Boutsinas M.N. Vrahatis P.A. Anninos University of Patras Artificial Intelligence Research Center Patras Greece Department of Medicine Medical Physics Laboratory Democritus University of Thrace Alexandroupolis Greece

We propose a novel method for the analysis of the magnetoencephalogram (MEG) of epileptic patients. The proposed method was based on the reconstruction of the phase space from the one-dimension signals to higher-dimension phase space. An especially developed clustering algorithm was applied on the reconstructed data in order to investigate the distribution of epileptic MEG dynamics in higher order reconstructed phase spaces.

关键词： Magnetic analysis Algorithm design and analysis Epilepsy clustering algorithms Heuristic algorithms Partitioning algorithms Superconducting magnets SQUIDs Nonlinear dynamical systems Machine learning algorithms

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CALSAGOS: clustering algorithms Applied to Galaxies in Overdense Systems

arXiv

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arXiv 2022年

作者： Olave-Rojas, D.E. Cerulo, P. Araya-Araya, P. Olave-Rojas, D.A. Departamento de Tecnologías Industriales Facultad de Ingeniería Universidad de Talca Los Niches km 1 Curicó Chile Departamento de Ingeniería Informática y Ciencias de la Computación Universidad de Concepción Chile Departamento de Astronomía Instituto de Astronomia Geofísica e Ciências Atmosféricas Universidade de São Paulo Cidade Universitária SP São PauloCEP: 05508-900 Brazil Instituto de Investigación Interdisciplinaria Universidad de Talca 1 Poniente 1141 Talca Chile

In this paper we present CALSAGOS: clustering algorithms Applied to Galaxies in Overdense Systems which is a PYTHON package developed to select cluster members and to search, find, and identify substructures. CALSAGOS is based on clustering algorithms and was developed to be used in spectroscopic and photometric samples. To test the performance of CALSAGOS we use the S-PLUS's mock catalogues and we found an error of 1% - 6% on member selection depending on the function that is used. Besides, CALSAGOS has a F1-score of 0.8, a precision of 85% and a completeness of 100% in the identification of substructures in the outer regions of galaxy clusters (r > r200). The F1-score, precision and completeness of CALSAGOS fall to 0.5, 75% and 40% when we consider all substructure identifications (inner and outer) due to the function that searches, finds, and identifies the substructures works in 2D and cannot resolve the substructures projected over others. Copyright © 2022, The Authors. All rights reserved.

关键词： clustering algorithms

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A survey on clustering algorithms for energy efficiency in wireless sensor network

A survey on clustering algorithms for energy efficiency in w...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Tuba Firdaus M. Hasan Dept. of Computer Sc. & Engg MMMUT Gorakhpur U.P. INDIA

ISBN: (纸本)9781467394178

In current scenario wireless sensor networks can be used in several areas like disaster management, intelligence surveillance and border security and so on. Wireless sensor networks (WSNs) are recognized as crucial and critical way to facilitate computing environment in effective and efficient manner for numerous applications. The most considerable constraint in wireless sensor network is limited energy. Wireless sensor networks (WSNs) play vital role in computing environment due to its energy saving attributes. In such applications, a collection of sensor nodes are required which are unattended and work independently. clustering is a significant technique which is applied in sensor networks to achieve better utilization of nodes. It is also helpful to get better load distribution in network. In this paper, we survey and discuss various dimensions and approaches of some broadly discovered algorithms for clustering. This paper also presents a comparative study of various clustering algorithms and discussion about the potential research areas and the challenges of clustering in WSNs.

关键词： Base stations Wireless sensor networks clustering algorithms Algorithm design and analysis Energy efficiency Protocols Fuzzy logic

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Rapid Prototyping of Hierarchical Agglomerative clustering algorithms for Distributed Systems

Rapid Prototyping of Hierarchical Agglomerative Clustering A...

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IEEE International Conference on Big Data

作者： Saiyedul Islam Navneet Goyal Sundar Balasubramaniam Poonam Goyal Achal Agarwal Kirti Singh Rathore Nischay Singh Advanced Data Analytics & Parallel Technologies Laboratory Birla Institute of Technology & Science Pilani Pilani Campus India

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Hierarchical Agglomerative clustering (HAC) algorithms are used in many applications where clusters have a hierarchical relationship between them. Their parallelization is challenging due to the dependence of every agglomeration step on all previous agglomerations. Although a few parallel algorithms have been proposed for SLINK HAC algorithm, only limited work has been done to parallelize other HAC algorithms. In this paper, we present a high-level abstraction, which provides a uniform way to specify any HAC algorithm, and a framework for automatic parallelization of the same for distributed memory systems. The abstraction is supported by constructs in a high level, domain specific language, and a compiler translates algorithms expressed in this language to efficient parallel code targeting distributed systems. Our experiments on multiple HAC algorithms proves that the runtime performance achieved is comparable with state-of-the-art manual parallel implementations on Spark and MPI while requiring only a fraction of the programming effort. At runtime, master-slave execution is used, and load is balanced among the slaves in an algorithm-agnostic way, which is a significant contrast to custom load-balancing techniques seen in the literature on parallel HAC algorithms.

关键词： clustering algorithms Programming Merging Heuristic algorithms DSL Measurement Manuals

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Research on degree of video completion of Internet videos with clustering algorithms

Research on degree of video completion of Internet videos wi...

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International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Jiawei He Yingyun Yang Anming Wei Wenhui Dong Communication University of China Beijing China State Administration of Press Publication Radio Film and Television Academy of Broadcasting Planning Beijing China

ISBN: (纸本)9781479998937

clustering algorithms are some of the most important algorithms used to describe data attributes and very effective ways to mine and analyze big data set. In this paper, the whole description of vector space of Internet users is acquired by clustering and analyzing user behavior data set. Moreover, all users are divided into different clusters according to KPI which is to correlate different users in terms of their degree of video completion. Our research shows that KPI of total length correlates degree of completion better than other KPIs. This KPI is drastically negatively correlated with user degree of video completion. Finally, we compare the accuracy and efficiency of three different algorithms which we used to cluster our research data in this paper.

关键词： clustering algorithms Streaming media Algorithm design and analysis Correlation clustering methods Internet TV

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Empirical comparison of fast clustering algorithms for large data sets

Empirical comparison of fast clustering algorithms for large...

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Annual Hawaii International Conference on System Sciences (HICSS)

作者： Chih-Ping Wei Yen-Hsien Lee Che-Ming Hsu Department of Information Management National Sun Yat-Sen University Kaohsiung Taiwan

ISBN: (纸本)0769504930

Several fast algorithms for clustering very large data sets have been proposed in the literature. CLARA is a combination of a sampling procedure and the classical PAM algorithm, while CLARANS adopts a serial randomized search strategy to find the optimal set of medoids. GAC-R/sup 3/ and GAC-RAR/sub w/ exploit genetic search heuristics for solving clustering problems. In this research, we conducted an empirical comparison of these four clustering algorithms over a wide range of data characteristics. According to the experimental results, CLARANS outperforms its counterparts both in clustering quality and execution time when the number of clusters increases, clusters are more closely related, more asymmetric clusters are present, or more random objects exist in the data set. With a specific number of clusters, CLARA can efficiently achieve satisfactory clustering quality when the data size is larger, whereas GAC-R/sup 3/ and GAC-RAR/sub w/ can achieve satisfactory clustering quality and efficiency when the data size is small, the number of clusters is small, and clusters are more distinct or symmetric.

关键词： clustering algorithms Sampling methods Genetic algorithms Data mining Information management Databases Decision making Association rules Pattern analysis Time series analysis

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Map building via integration of fuzzy systems and clustering algorithms

Map building via integration of fuzzy systems and clustering...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Y.L. Ip A.B. Rad Y.K. Wong Department of Electrical Engineering Hong Kong Polytechnic University Hung Hom Hong Kong China

This paper presents a segment detection and grouping scheme that allows incremental and online learning of indoor environment maps by mobile robots. In this study, the modeling is refined by first dividing the world into discrete regions as local models. The line segments in local models are extracted by clustering algorithm. The local models are grouped together by a hierarchical fuzzy system. Adjusting the membership functions that establish the grouping criteria controls the degree of approximation in such combination. The performance of the algorithm is validated in indoor office environments using a Pioneer II mobile robot.

关键词： Fuzzy systems clustering algorithms Mobile robots Solid modeling Indoor environments Electrical equipment industry Humans Sonar Uncertainty Employment

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An introduction to biological wastewater treatment explained by SOM and clustering algorithms

An introduction to biological wastewater treatment explained...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： H.L. Garcia I.M. Gonzalez Department de Ingeniería Eléctrica Electrónica de Computadores y Sistemas. Edificio Department 2 Zone Oeste Escuela Politécnica Superior de Ingeniería Gijon Spain

The objective of this paper is the establishment of the relationships among the main variables of a wastewater biological treatment process using, as a first approach, data from a continuous aerobic reactor that was simulated by means of the activated sludge model No 1 (ASM no 1). Self-organizing map (SOM) and clustering techniques were used to carry out this task.

关键词： Wastewater treatment clustering algorithms Inductors Biological system modeling Biomass Biodegradable materials Microorganisms Organisms Nitrogen Biological materials

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Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay clustering algorithms

Research Square

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Research Square 2021年

作者： Kumar, V. Jayapandian, N. Balasubramanie, P. Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Christ University Bangalore India Department of Computer Science and Engineering Kongu Engineering College Perundurai India

Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide the best method of attachments are included. The Problem coverage find changeability network route due to which traffic and delays keep the performance to be very high. A newer version of Gravity Analysis Algorithm (GAA) is used to solve this problem. This proposed new approach GAA is introduced to improve network lifetime, increase system energy efficiency and end delay performance. Simulation results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay clustering algorithms-LTDCA protocols. The proposed method provides a set of data collection and increased throughput in wireless sensor networks. © 2021, CC BY.

关键词： clustering algorithms

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