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检索条件"主题词=K-means clustering algorithm"

共 398 条记录，以下是301-310 订阅

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Unsupervised Subjects Classification Using Insulin and Glucose Data for Insulin Resistance Assessment 20

Unsupervised Subjects Classification Using Insulin and Gluco...

引用

20th SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA)

作者： Altuve, Miguel Severeyn, Erika Wong, Sara Pontifical Bolivarian Univ Fac Elect Engn Bucaramanga Colombia Univ Simon Bolivar Appl Biophys & Bioengn Grp Caracas Venezuela Univ Cuenca DEET Investigador Prometeo Cuenca Ecuador

ISBN: (纸本)9781467394611

In this paper, the k-means clustering algorithm is employed to perform an unsupervised classification of subjects based on unidimensional observations (HOMA-IR and the Matsuda indexes separately) and multidimensional observations (insulin and glucose samples obtained from the oral glucose tolerance test). The goal is to explore if the clusters obtained could be used to predict or diagnose insulin resistance or are related to the profiles of the population under study: metabolic syndrome, marathoners and sedentaries. Using two and three clusters, three classification experiments were carried out: i) using the HOMA-IR index as unidimensional observations, ii) using the Matsuda index as unidimensional observations, and iii) using five insulin and five glucose samples as multidimensional observations. The results show that using the HOMA-IR index the clusters are related to insulin resistance but when multidimensional observations are used in the classification process the clusters could be used to predict the insulin resistance or other related diseases.

关键词： diseases medical computing pattern classification pattern clustering unsupervised learning HOMA-IR index k-means clustering algorithm Matsuda index disease glucose data insulin data insulin resistance assessment unsupervised subject classification clustering algorithms Diseases Immune system Indexes Insulin Sugar Unsupervised learning Unsupervised learning maladjustment clustering algorithms medical computing Insulin Insulin Resistance Pattern recognition Insulins Cluster Analysis Immune System sugar Prediabetic State

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An Approach of Lane Detection Based on Inverse Perspective Mapping 17

An Approach of Lane Detection Based on Inverse Perspective M...

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IEEE 17th International Conference on Intelligent Transportation Systems (ITSC)

作者： Wang, Jun Mei, Tao kong, Bin Wei, Hu Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

ISBN: (纸本)9781479960781

Urban lane detection is an essential task for unmanned vehicle system. This paper describes an approach of lane detection algorithm based on Inverse Perspective Mapping, first using overall optimal threshold method to obtain binary image for reducing noise;next using Inverse Perspective Mapping to transform binary image space to top view space;then using k-means clustering algorithm to analysis linear discriminant for reducing interference affect;finally, fitting lane discontinuous on the top view space according road models. Experimental results are presented to demonstrate the effectiveness and superiority of the urban lane detection algorithm.

关键词： binary image overall optimal threshold method Inverse Perspective Mapping k-means clustering algorithm road models

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Research into Data Mining algorithms Based on Cloud Computing Environments

Research into Data Mining Algorithms Based on Cloud Computin...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Song, Yu Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

Focusing on the Hadoop architecture in a cloud computing environment and based on the distributed Hadoop system, the MapReduce model and the HBase database, an improved k-means clustering algorithm based on the Hadoop framework was developed, the original cluster analysis designer was improved, data elements were extracted and an analysis model for hot topics in the network was created, and simulation experiments with different databases were completed. The simulation experiments on different datasets and the data mining results in terms of the sampling interval and system accuracy with the improved k-means clustering algorithm suggest that the algorithm is more suitable for data mining in cloud computing and that it has some advantages. © 2023 IEEE.

关键词： cloud computing environment data mining k-means clustering algorithm

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The Research and Realization of clustering algorithm Based On FPGA

The Research and Realization of Clustering Algorithm Based O...

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2008年国际电子商务、工程及科学领域的分布式计算和应用学术研讨会

作者： Jun Feng Wenbo Xu Zhilei Chai College of Information,Jiangnan University,Wuxi,214122,China

clustering algorithm is an important method of researching neural *** paper introduces a method to implement this algorithm,under the FPGA, implement k-means clustering algorithm,accelerating the arithmetic by hardware,more rapidness,more *** result shows that implement this algorithm take advantage of hardware better than software.k-means clustering algorithm is a kind of clustering algorithm which can be dealt with FPGA, because it is a parallel algorithm,it will be completely get faster by FPGA than it used to *** that is all the reason why we implement k-means clustering algorithm by FPGA.

关键词： FPGA Neural Network k-means clustering algorithm

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Research on the Text clustering algorithm based on Latent Semantic Analysis and Optimization

Research on the Text Clustering Algorithm based on Latent Se...

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2011 IEEE International Conference on Computer Science and Automation Engineering(CSAE 2011)

作者： Wang Chun-hong Nan Li-Li Ren Yao-Peng Computer Science and Technology ,Yun cheng University, Yun Cheng,044000

The text clustering based on Vector Space Model has problems, such as high-dimensional and sparse, unable to solve synonym and polyseme etc. And meanwhile, k-means clustering algorithm has shortcomings, which depends on the initial clustering center and needs to fix the number of clusters in advance. Aiming at these problems, in this paper, a text clustering algorithm based on Latent Semantic Analysis and Optimization is proposed. This algorithm can not only overcome the problems of Vector Space Model, but also can avoid the shortcomings of k-means algorithm. And compared with the text clustering algorithm based on Latent Semantic Analysis and the text clustering algorithm based on Vector Space Model and optimization, our algorithm is proved which can preferably improve the effect of text clustering, and upgrade the precision ratio and recall ration of text.

关键词： text clustering Vector Space Model Latent Semantic Analysis clustering optimization k-means clustering algorithm

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Application on query of Distributed Database Based on Improved Genetic algorithm

Application on query of Distributed Database Based on Improv...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Chen Fuxing Xie Xusheng Jiangxi Normal Univ Coll Comp Informat Engn Nanchang Peoples R China

ISBN: (纸本)9783038351153

The query cost usually as an important criterion for a distributed database. The genetic algorithm is an adaptive probabilistic search algorithm, but the crossover and mutation probability usually keep a probability in traditional genetic algorithm. If the crossover probability keep a large value, the possibility of damage for genetic algorithm model is greater;In turn, if the crossover probability keep a small value, the search process will transform a slow processing or even stagnating. If the mutation probability keep a small value, a new individual can be reproduced difficultly;In turn, if the mutation probability keep a large value, the genetic algorithm will as a Pure random search algorithm. To solve this problem, proposed a improved genetic algorithm that multiple possibility of crossover and mutation based on k-means clustering algorithm. The experiment results indicate that the algorithm is effective.

关键词： distributed database genetic algorithm distributed query optimization k-means clustering algorithm

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Research on Ocean Adaptation Zone Classification Prediction Model Based on Gravity Anomaly data 2

Research on Ocean Adaptation Zone Classification Prediction ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： He, Chenkai He, Hongyi Chen, Yiru Dalian Polytechnic University School of Food Science and Technology Liaoning Dalian China Dalian Polytechnic University School of Biological Engineering Liaoning Dalian China

ISBN: (纸本)9798350317558

This paper aims to propel the development of the marine economy by proposing a crucial underwater navigation adaptation zone classification prediction technique. Firstly, through meticulous processing of gravity anomaly benchmark data, the maritime area is delineated, and adaptability is calibrated to ensure the navigation precision of underwater vehicles. Using the k-means classification model, the data is refined and categorized into zones of medium-high accuracy, low accuracy, medium accuracy, and high accuracy. Subsequently, specific feature attribute indicators are selected, and an effective regional adaptation zone classification prediction model system, denoted as F, is established to ensure navigation accuracy within regions with different gravity anomaly feature distributions. Finally, transferability predictions are performed on new gravity anomaly data to explore the model's applicability to novel data, validating the robustness and generalization capability of the model and fostering further advancements in underwater navigation technology. This research holds significant importance for driving the marine economy forward. © 2024 IEEE.

关键词： Data-driven interpolation processing k-means clustering algorithm Python SVM algorithm

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Detection of Traffic Abnormity Based on clustering Analysis of Taxi GPS Data

Detection of Traffic Abnormity Based on Clustering Analysis ...

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作者： Haoze Wang Yutong Si Beijing Jiaotong University Beijing University of Posts and Telecommunications

The paper proposes a method to detect the traffic abnormity based on clustering analysis of taxi GPS data. The traffic data used in this paper is obtained from traffic management bureau of *** data is cleaned based on several cleaning standards to eliminate error and noise data. Map-matching algorithm is used to match the trace points to the specific roads according to OSM map data. Non-Negative Matrix Factorization(NMF) is applied to extract the traffic patterns. In order to build abnormity detection model. k-means Cluster algorithm is applied to find the neighbor roads based on both physical distance and traffic pattern similarity.A scoring function is designed to provide the probability for the specific road to be abnormal. The result of this method is quite accurate as it provides the detection results by means of horizontal and vertical comparisons. Some comparisons with other existing methods will also be discussed at the end of paper.

关键词： GPS data map matching k-means clustering algorithm NMF non-negative matrix factorization scoring function traffic abnormity traffic analysis

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Research on intrusion detection based on feature extraction of autoencoder and the improved k-means algorithm

Research on intrusion detection based on feature extraction ...

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作者： Wang, Xingang Wang, Linlin School of Information Qilu University of Technology Jinan China

ISBN: (纸本)9781538636749

Nowadays, intrusion detection is a technology to effectively avoid a number of risks of network intrusion. The k-means algorithm is widely used in intrusion detection. But, the algorithm has some shortcomings, such as random selection of k value, sensitive selection of initial cluster centers, and low accuracy in clustering high-dimensional data. In order to make up for these shortcomings of the k-means algorithm, this paper proposes an AE-kmeans architecture which combines an autoencoder with the improved k-means algorithm. The AE-kmeans architecture realizes the dimension reduction and feature extraction of these original data by introducing an autoencoder, and uses the improved k-means algorithm to cluster these processed data. These improvements of the improved k-means algorithm mainly include two aspects: the first aspect, a new method is introduced to select initial cluster centers;the second aspect, the algorithm calculates the weight of each attribute with the coefficient of variation, and then uses these weights in Euclidean distance formula, resulting in a weighted Euclidean distance formula. Finally, the AE-kmeans architecture uses kDD CUP99 data set for intrusion detection simulation experiments. The Experimental results show that the AE-kmeans architecture not only enhances the ability to deal with high-dimensional data, but also improves the detection rate and reduces the error-detection rate compared with k-means algorithm. © 2017 IEEE.

关键词： AE-kmeans architecture Autoencoder Feature extraction Intrusion detection k-means clustering algorithm Weighted Euclidean distance formula

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Prenatal Detection of Ventricular Septal Defects by VGG -16 Model Using Ultrasound Images 3

Prenatal Detection of Ventricular Septal Defects by VGG -16 ...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： kavitha, D. Lakshmi, C.R. CMR Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru India Cambridge Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350317022

Congenital heart defect is one of the important anomaly. The deformed heart can be differentiated from the normal heart based on several parameters such as the size of auricles, ventricles, valve and position of heart, area, circumference, and perimeter. One of the methods to detect the anomalies in fetal heart is by applying advanced image pprocessing techniques and artificial intelligence algorithms. This proposed system is the primary framework for diagnosing the prenatal ventricular septal defects (PVSD). The first step is to denoise the US images using enhanced anisotropic diffusion Enhanced Perona Malik Filter (EPMF), followed by k-means clustering segmentation method as the second step, and finally, VGG-16 architecture was implemented with the pre-trained weights from the database. The original image is compared with the reference image in terms of different parameters using a VGG16 deep learning algorithm to predict PVSD anomalies at the early stage of pregnancy. The experimental results shows that PSNR reach to high value of 33.45, Dice coefficient and jacquard index and conformity coefficient as 0.88-0.86. VGG-16 is the first attempt to diagnose prenatal ventricular septal defects to achieve an accuracy of 90%. This proposed system will give a second opinion for the doctors in diagnosing the abnormalities at the early stages. © 2023 IEEE.

关键词： Enhanced Perona Malik filter k-means clustering algorithm VGG-16 CNN model

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