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Comparison and Detection Analysis of Network Traffic Datasets Using k-means clustering algorithm

JOURNAL OF INFORMATION & kNOWLEDGE MANAGEMENT 2020年第3期19卷

作者： Al-Sanjary, Omar Ismael Bin Roslan, Muhammad Aiman Helmi, Rabab Alayham Abbas Ahmed, Ahmed Abdullah Management & Sci Univ Fac Informat Sci & Engn Shah Alam 40100 Malaysia Qaiwan Int Univ QIU Fac Engn & Sci Sulaymaniyah Kurdistan Regio Iraq

Anomaly detection in specific datasets involves the detection of circumstances that are common in a homogeneous data. When looking at network traffic data, it is generally difficult to determine the type of attack without proper analysis and this holds true when simply viewing a record of network traffic with thousands of internet users to detect malicious activity. However, there are different types of datasets in light of the way they record or acquire data and facts. The paper aims to compare and analyse multiple datasets including NSL-kDD and MAWI by using k-means clustering algorithm. Specifically, the paper analyses the blind-Spots of the datasets and evaluates the most suitable dataset for k-means clustering algorithm. This paper's quantitative data analysis results are helpful in evaluating weaknesses of each dataset of traffic data, when using k-means clustering algorithm.

关键词： NSL-kDD MAWI k-means clustering algorithm

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An Evaluation Strategy of Energy Storage Construction for Industrial Users Based on k-means clustering algorithm

An Evaluation Strategy of Energy Storage Construction for In...

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IEEE International Conference on Computing, Communications and IoT Applications (IEEE ComComAp)

作者： Zhao, Yangyang Deng, Boya Chen, Hui Zhang, Hualu Shi, Jie Xu, Zhengwei Zhu, Xingyang Han, Guangjie State Grid Suzhou City Energy Res Inst Co Ltd Suzhou Peoples R China State Grid Suzhou Power Supply Co Ltd Suzhou Peoples R China NARI Informat & Commun Technol Co Ltd Nanjing Peoples R China Hohai Univ Dept Informat & Commun Syst Changzhou Jiangsu Peoples R China

ISBN: (纸本)9781728119731

Energy storage refers to a series of related technologies that achieve energy reserve and release by some ways when needed. Energy storage technology plays an important role in peak shaving and valley filling, improving the equipment utilization efficiency, enhancing the system safety and so on. It has great significances to realize the transformation of energy structure and the change of electricity production or consumption patterns for promoting the overall development of energy industry through the research of user-side energy storage technology. In this paper, we analyze the historical power consumption data of industrial users by k-means clustering algorithm to design a user type automatic evaluation model, which can assist large industrial users to judge whether industrial users are suitable for energy storage construction or not, it can help enterprises save manpower and financial resources. In addition, we design a method to calculate the potential value of users, which can reflect the degree of suitability for equipping energy storage device. Simulation results show that the proposed model can correctly obtain the users' typical load curve and power usage type through clustering historical power load data.

关键词： k-means clustering algorithm Energy storage Peak shaving and valley filling

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Dynamic Equivalent Modeling of Induction Motors based on k-means clustering algorithm

Dynamic Equivalent Modeling of Induction Motors based on K-m...

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IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia)

作者： Lin, Xueshan Wu, Hongbin Liu, Xin Xu, Bin Hefei Univ Technol Sch Elect Engn & Automat Hefei Anhui Peoples R China State Grid Anhui Elect Power Res Inst Hefei Anhui Peoples R China

ISBN: (纸本)9781538674345

It is unrealistic to establish the model for analyzing dynamic characteristic of each induction motor. In order to ensure that the equivalent induction motor models impose similar impact on the electric system compared with the original induction motors group, the critical slip scr and the load rate kL of the induction motor were taken as the clustering indicators in this paper. Induction motors group was clustered based on the k-means algorithm to get different cluster numbers. Then the optimal clustering result was obtained by the F statistics. After the clustering, induction motors in different classes were equalized and the accurate dynamic equivalent models were established. The simulation curves were obtained after comparing the established equivalent models with the original models and the classic models in the short circuit fault condition through the example system. The simulation results proved that the method proposed in this paper were reliable.

关键词： Dynamic equivalence electromotor group k-means clustering algorithm modeling

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School-Based Management Performance Efficiency Modeling and Profiling using Data Envelopment Analysis and k-means clustering algorithm 4

School-Based Management Performance Efficiency Modeling and ...

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4th IEEE International Conference on Computer and Communication Systems (ICCCS)

作者： Tibay, Jona P. Ambat, Shaneth C. Lagman, Ace C. AMA Univ Sch Grad Studies Quezon City Philippines FEU Inst Technol Manila Philippines

ISBN: (纸本)9781728113227

Organizations are challenged to achieve effective and competent results, rising to imminent importance of measuring the performance efficiency. Data Envelopment Analysis (DEA) is an approach that measures performance efficiency of organizations. It is a non-parametric method, which uses linear programming to calculate efficiency in a given set of decision-making units (DMUs). It has widespread application in identifying efficiency and discovering benchmark. In the study, it utilized DEA in identifying School-Based Management (SBM) performance efficiency of one (1) division comprising of elementary and secondary schools under Department of Education (DepEd) in the Philippines. Efficient schools were used as benchmark for improvement of inefficient schools. The schools had also undergone clustering, which is the process of grouping in accordance to similar characteristics. k-means clustering algorithm was used to group the schools according to their respective profile. k-means clustering is a simple unsupervised learning algorithm that follows a simple procedure of classifying a given data set into a number of clusters. The study also encompasses the development of an application system that utilizes data from DEA and k-means clustering algorithm. The application system also provided recommendations to help inefficient schools improve.

关键词： Data Envelopment Analysis (DEA) k-means clustering algorithm Performance Efficiency

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An Enhanced Regularized k-means Type clustering algorithm With Adaptive Weights

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IEEE ACCESS 2020年 8卷 31171-31179页

作者： Wu, Ziheng Wu, Zixiang Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243000 Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 2 Dept Thorac Surg Hangzhou 310000 Peoples R China

$k$ -means clustering algorithm is one of the most popular technique for clustering in machine learning, however, in the existing $k$ -means clustering algorithm, the ability of the different features and the importance of the different data objects are treated equally;the discriminative ability of the different features and the importance of the different data objects cannot be differentiated effectively. In the light of this limitation, this paper put forward an enhanced regularized $k$ -means type clustering algorithm with adaptive weights in which we introduced an adaptive feature weights matrix and an adaptive data weights vector into the objective function of the $k$ -means clustering algorithm and we developed a new objective function with $l<^>clustering$ -norm regularization to the weights of data objects and features, then we obtained the corresponding scientific updating iterative rules of the weights of the different features, the weights of the different data objects and the cluster centers theoretically. In order to evaluate the performance of the new algorithm put forward, extensive experiments were conducted. Experimental results have indicated that our proposed algorithm can improve the clustering performance significantly and are more effective with respects to three metrics: the successful clustering rate (SCR), normal mutual information (NMI) and RandIndex.

关键词： k-means clustering algorithm machine learning adaptive weights l(2)-norm regularization

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Research on MapReduce Parallel Optimization Method Based on Improved k-means clustering algorithm

Research on MapReduce Parallel Optimization Method Based on ...

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作者： Ye Xiong Qingyu Peng Zhenhang Zhang School of Automation & Electrical Engineering Lanzhou Jiaotong University Construction Second Division CMCU Engineering Co. Ltd. School of Electrical Engineering Chongqing University

The traditional k-means clustering algorithm occupies a large quantity of memory resources and computing costs when dealing with massive data. It is easy to be restricted by something such as the initial center point as well abnormal data, and usually can not achieve effective clustering of large-scale data. In order to effectively solve the limitations of the algorithm, we propose a MapReduce parallel optimization method based on improved k-means clustering algorithm. Firstly, differential evolution theory is introduced to determine the optimal initial clustering center, after that, on the basis of the influence of samples on clustering results, the corresponding weighted Euclidean distance is designed to achieve effective data differentiation, so as to effectively reduce the impact of samples on clustering *** negative effect of abnormal data on clustering analysis can improve the accuracy of clustering. Finally, MapReduce programming model is used to realize parallel clustering. We use UCI datasets to verify the parallel optimization method. From the experimental results we can clearly know that the method we proposed has relatively stable parallel clustering results, faster operation speed, and effectively saves the operation time.

关键词： k-means clustering algorithm MapReduce programming model Differential evolution algorithm UCI dataset Initial clustering center Weighted Euclidean distance

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Mitigate the impact of transmitter finite extinction ratio using k-means clustering algorithm for 16QAM signal

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OPTICS COMMUNICATIONS 2018年 409卷 72-76页

作者： Yu, Miao Li, Yan Shu, Tong Zhang, Yifan Hong, Xiaobin Qiu, Jifang Zuo, Yong Guo, Hongxiang Li, Wei Wu, Jian Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing Peoples R China

A method of recognizing 16QAM signal based on k-means clustering algorithm is proposed to mitigate the impact of transmitter finite extinction ratio. There are pilot symbols with 0.39% overhead assigned to be regarded as initial centroids of k-means clustering algorithm. Simulation result in 10 GBaud 16QAM system shows that the proposed method obtains higher precision of identification compared with traditional decision method for finite ER and IQ mismatch. Specially, the proposed method improves the required OSNR by 5.5 dB, 4.5 dB, 4 dB and 3 dB at FEC limit with ER= 12 dB, 16 dB, 20 dB and 24 dB, respectively, and the acceptable bias error and IQ mismatch range is widened by 767% and 360% with ER = 16 dB, respectively. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Extinction ratio k-means clustering algorithm Quadrature amplitude modulation Coherent optical communication

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Optimal Cell Screening Method for Minimizing the Imbalance Based on k-means clustering algorithm 21

Optimal Cell Screening Method for Minimizing the Imbalance B...

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21st International Conference on Electrical Machines and Systems (ICEMS)

作者： Lee, Pyeong-Yeon Han, Dong-Ho kim, Gun-Woo kang, Tae-Woo Pack, Sung-Baek kim, Jonghoon Chungnam Natl Univ Elect Engn Daejeon South Korea Korea Inst Nucl Safety Nucl Safety Res Team Daejeon South Korea

ISBN: (纸本)9788986510201

An imbalance of the battery pack in the voltage and state of charge(SOC) leads to problems in the lifetime, performance, and safety. To efficiently operate the battery pack in the power-driven systems, each cell in the battery pack has similar electrochemical characteristics. This paper proposes the optimal cell screening method using k-means clustering algorithm, which is one of the unsupervised learning methods. To investigate the proposed scheme, 2000 units of 18650 Li-ion cell are used in this paper. The results of the verification demonstrate that the proposed screening method minimizes the cell imbalance.

关键词： k-means clustering algorithm li-ion battery internal resistance cell screening

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Information Architecture (IA): Using Multidimensional Scaling (MDS) and k-means clustering algorithm for Analysis of Card Sorting Data

JOURNAL OF USABILITY STUDIES

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JOURNAL OF USABILITY STUDIES 2018年第3期13卷 138-157页

作者： Paea, Sione Baird, Ross Univ South Pacific Sch Comp Informat & Math Sci Suva Fiji Optimal Workshop Www Optimalworkshop Com Wellington New Zealand

We present a method for visualizing and analyzing card sorting data aiming to develop an in-depth and effective information architecture and navigation structure. One of the well-known clustering techniques for analyzing large data sets is with the k-means algorithm. However, that algorithm has yet to be widely applied to analyzing card sorting data sets to measure the similarity between cards and result displays using multidimensional scaling. The multidimensional scaling, which employs particle dynamics to the error function minimization, is a good candidate to be a computational engine for interactive card sorting data. In this paper, we apply the combination of a similarity matrix, a k-means algorithm, and multidimensional scaling to cluster and calculate an information architecture from card sorting data sets. We chose card sorting to improve an information architecture. The proposed algorithm handled the overlaps between cards in the card sorting data quite well and displayed the results in a basic layout showing all clusters and card coordinates. For outliers, the algorithm allows grouping of single cards to their closest core clusters. The algorithm handled outliers well choosing cards with the strongest similarities from the similarity matrix. We tested the clustering algorithm on real-world data sets and compared to other techniques. The results generated clear knowledge on relevant usability issues in visualizing information architecture. The identified usability issues point to a need for a more in-depth search of design solutions that are tailored for the targeted group of people who are struggling with complicated visualizing techniques. This study is for people who need support to easily visualize information architecture from data sets.

关键词： information architecture card sorting multidimensional scaling k-means clustering algorithm analysis similarity matrix

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A k-means clustering algorithm for Automatically Obtaining k Value

A K-means Clustering Algorithm for Automatically Obtaining K...

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2018 3rd International Conference on Electrical, Control and Automation Engineering (ECAE 2018)

作者： Yuan-qiang XIE Rui-ming FANG College of Information Science and Engineering Huaqiao University

In order to solve the problem of manual input of k value in traditional k-means clustering algorithm, a method to obtain k value automatically is proposed. Firstly, the data needed to be clustered are sampled, and the distance between the data in the sample group is calculated, Then the distance matrix is formed and the de-noising process is done, and the clustering number k is selected based on the principle of distance maximization. Finally, we take two-dimensional data as an example to simulate the k-means clustering algorithm of the number of clusters k based on the principle of distance maximization, then the simulation results are verified by MATLAB. The experimental results show that the algorithm can obtain k value automatically and improve the accuracy of clustering.

关键词： k-means clustering algorithm Distance matrix Distance maximization Automatic acquisition

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