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

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

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

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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-...

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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

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

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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

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Research on Hybrid Data Clustering algorithm for Wireless Communication Intelligent Bracelets

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MOBILE NETWORkS & APPLICATIONS 2023年第5期28卷 1762-1771页

作者： Sun, Jian-zhao Yang, kun Wozniak, Marcin Henan Inst Econ & Trade Sch Comp Engn Zhengzhou 450000 Henan Peoples R China Zhengzhou 7 Peoples Hosp Dept Med Zhengzhou 450000 Henan Peoples R China Silesian Tech Univ Fac Appl Math PL-44100 Gliwice Poland

Wireless communication smart bracelet data include motion data, sleep time data, heart rate and blood pressure data and positioning data, etc. These data have diversity and high complexity, and there are interconnections or interactions between the data, which have high clustering difficulty. To this end, a new data clustering algorithm is studied for wireless communication smart bracelets. The k-medoids algorithm is used to calculate the intra-cluster, inter-cluster, or overall similarity to complete the initial clustering of the bracelet data. Setting the clustering evaluation index can determine the optimal number of clusters. The data objects that are closely surrounded and relatively dispersed are selected as the initial clustering centers and combined with the new index IXB to complete the improvement of the data clustering algorithm. The test results show that the accuracy, recall, and F1 of the research algorithm for clustering the heart rate monitoring dataset, temperature monitoring dataset, energy consumption dataset, and sleep monitoring dataset are higher than 97%, which indicates that the data clustering effect of the algorithm is good.

关键词： Wireless communication smart bracelet k-medoids algorithm Internal evaluation index Clustering center

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

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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

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

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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)

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Optimal Coordination of Distance Protection Considering Multiple Setting Groups Strategy Based on Sideline Current Addition Coefficient 12

Optimal Coordination of Distance Protection Considering Mult...

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12th International Conference on Power and Energy Systems (ICPES)

作者： Ren, kangjie Li, Yinhong Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan Peoples R China

ISBN: (纸本)9781665451451

In order to improve the adaptability of distance protection settings to extreme operation modes, an optimal coordination method of distance protection considering multiple setting groups strategy is proposed, which is based on sideline current addition coefficients (SCAC). A clustering model based on k-medoids algorithm is established, with the SCAC as the characteristic of the operation mode. With the goal of the minimum action time of the whole network and the lowest loss of selectivity probability, the Multiple Objective Particle Swarm Optimization (MOPSO) algorithm is used to optimize coordination of distance protection under the operation mode in each cluster group. The IEEE 39-bus system verifies the effectiveness of the proposed method.

关键词： multiple setting groups strategy k-medoids algorithm extreme operation mode distance protection MOPSO

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Skewness and access kurtosis as denoised mixed features-based k-medoids for cooperative spectrum sensing

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PHYSICAL COMMUNICATION 2022年第0期54卷

作者： Radhi, Ali A. Akkar, Hanan A. R. Abdullah, Hikmat N. Univ Technol Baghdad Dept Elect Engn Baghdad Iraq Al Nahrain Univ Dept Informat & Commun Engn Baghdad Iraq

A traditional cooperative spectrum sensing system based on the energy features of random matrix theory does not function well when the signal-to-noise ratio is low in the Gaussian noise channel. In this paper, the poor performance of this system is improved. A novel blind cooperative spectrum sensing scheme based on new denoised mixed-features exploiting the k-medoids algorithm has been developed. In the first place, a new mathematical formula is proposed to extract denoised mixed -features and decrease the amount of noise in the received signals of each secondary user. This formula is expressed as the square root of the sum of the squares of the third and fourth-highest order moments of skewness (S-k) and access kurtosis (k-u-3). In the fusion center, two new denoised mixed-features methods are proposed. The first method is based on calculating the Average values of S-k and k-u-3 of the denoised mixed features vectors, we named it the ASkU3 method. The second method is based on calculating the Maximum values of S-k and k-u-3 of the denoised mixed features vectors, we named it the MSkU3 method. In comparison to the existing energy feature-based methods, the denoised mixed-features-based on highest-order moments avoid the computational complexity associated with threshold estimation. Furthermore, they mitigate the effect of noise uncertainty and improve sensing performance by reducing the overlap between clusters of the k-medoids algorithm. Then, the k-medoids method makes use of the newly formed methods in order to train the classifier effectively depending on whether the primary user is present or not. Finally, several energy-feature-based methods classified by the clustering algorithm are compared in this study. Moreover, the theoretical and practical performances of the denoised mixed-features methods, ASkU3 and MSkU3, are very well matched. The simulation findings show that the proposed methods have improved the effectiveness of sensing at low signal-to-noise ra

关键词： Skewness and kurtosis moments Denoised mixed features k-medoids algorithm

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CLUSTERING algorithm IN DIGITAL MANAGEMENT AND SUSTAINABLE SYSTEM CONSTRUCTION FOR URBAN RAIL TRANSPORTATION STUDENT EDUCATION

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第1期25卷 241-253页

作者： Li, Yijia Hunan Vocat Coll Railway Technol Coll Railway Transportat Management & Econ Zhuzhou 412000 Peoples R China

With the rapid growth of the national economy, people's demand for transportation is becoming increasingly strong. The rail transit business is booming in large and medium-sized cities, and the education management of urban rail transit students needs further reform. At the same time, digital information technology is widely used in various fields, and digital management of education has become one of the major development directions of education reform. The study proposes a specific construction path based on the analysis of the necessity of digital management of education for urban rail transportation majors, and then optimizes the k-medoids algorithm in the clustering algorithm and validates its education digital management effect. The outcomes show that the clustering precision of the upgraded k-medoids algorithm in the selected dataset is up to 92.68%, and the running time is all below 5s, with the lowest value being 3.9s;In the digital management of urban rail transit majors in universities, the precision obtained by the algorithm is all maintained at around 95%, and the satisfaction rate is all higher than 90%. The effectiveness of the proposed method has been verified, providing a new method for the management of digital education systems for urban rail transit students. It can better understand the needs and characteristics of students, help improve their learning effectiveness and educational quality, and achieve more targeted allocation of educational resources.

关键词： Internet of Things path optimization time windows dynamic demand Clustering k-medoids algorithm Rail transportation Education Digital management cost

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Study on a hybrid algorithm combining enhanced ant colony optimization and double improved simulated annealing via clustering in the Traveling Salesman Problem (TSP)

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1609页

作者： Hao, Tan Wu Yingnian Zhang Jiaxing Jing, Zhang Beijing Informat Sci & Technol Univ Sch Automat Beijing Peoples R China

In the process of solving the Traveling Salesman Problem (TSP), both Ant Colony Optimization and simulated annealing exhibit different limitations depending on the dataset. This article aims to address these limitations by improving and combining these two algorithms using the clustering method. The problems tackled include Ant Colony Optimization's susceptibility to stagnation, slow convergence, excessive computations, and local optima, as well as simulated annealing's slow convergence and limited local search capability. By conducting tests on various TSPLIB datasets, the algorithm proposed in this article demonstrates improved convergence speed and solution quality compared to traditional algorithms. Furthermore, it exhibits certain advantages over other existing improved algorithms. Finally, this article applies this algorithm to logistics transportation, yielding excellent results.

关键词： k-medoids algorithm The ant colony algorithm The simulated annealing algorithm 2-opt operator Logistics and transportation

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