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检索条件"主题词=K-means Algorithm"

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Comparison of Hybrid ACO-k-means algorithm and Grub cut for MRI images segmentation 14

Comparison of Hybrid ACO-k-means algorithm and Grub cut for ...

引用

14th International Symposium on Intelligent Systems

作者： El-khatib, S. A. Skobtsov, Y. A. Rodzin, S., I Southern Fed Univ 105-42 Bolshaya Sadovaya str Rostov Na Donu 344006 Russia St Petersburg State Univ Aerosp Instrumentat 67 Bolshaya Morskaya Str St Petersburg 190000 Russia

Image segmentation is the process of dividing image into homogenous regions by some charasteristics and is widely used in medical diagnostics. Segmentation algorithms are used for anatomical features extraction from medical images. The Hybrid Ant Colony Optimization (ACO) k-means and Grub Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. As there is no universal algorithm for medical image segmentation, image segmentation is still a challenging problem in image processing and computer vision in many real time applications and hence more research work is required. The experimental results show that the proposed algorithm has good accuracy in comparison to Grub cut. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： MRI image segmentation ant colony optimization k-means algorithm swarm intelligence grub cut

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The research on customer classification of B2C platform based on k-means algorithm 3

The research on customer classification of B2C platform base...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Li, Xiangtao Li, Chen Beijing Informat Sci & Technol Univ Beijing Peoples R China

ISBN: (纸本)9781538645093

Unlike the traditional customer relationship management, e-commerce can not only retain transaction data, but also collect other customer information, such as browsing data, comments, and preferences. In order to solve the problem of B2C e-commerce customer classification, k-means algorithm was selected as the clustering classification method, and an improved RFM value model was proposed to add indicators that meet the e-commerce domain characteristics. The customer group in the B2C environment is well classified.

关键词： E-commerce Customer classification RFM model k-means algorithm

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Smart Health Predicting System Using k-means algorithm 10

Smart Health Predicting System Using K-Means Algorithm

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10th International Conference on Computer Communication and Informatics (ICCCI)

作者： Ravuvar, Harshitha Naidu Goda, Haritha Sumathi, R. Chinnasamy, P. Kalasalingam Acad Res & Educ Dept Comp Sci & Engn CSE Krishnankoil Tamil Nadu India Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Krishnankoil Tamil Nadu India Sri Shakthi Inst Engn & Technol Dept Informat Technol Coimbatore Tamil Nadu India

ISBN: (数字)9781728145143

ISBN: (纸本)9781728145136

This main goal of this Appis mainly focus on the predicting the disease based on the given symptoms by the user through registration. The patient can able to get the doctor details and address of the hospital immediately by giving the disease and particular location where he requires. He gets the appointment from various doctors for respective treatments. The user can able to know the information regarding the acceptance and rejection of their appointment. If the appointment is accepted simultaneously, the bills can be sent to the respective user and he can pay the amount through this app.

关键词： Health Prediction Symptoms k-means algorithm Doctor Appointment Location

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Fault Location of Hybrid Transmission Lines Based on k-means algorithm

Fault Location of Hybrid Transmission Lines Based on k-means...

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China International Conference on Electricity Distribution (CICED)

作者： Shang Liqun Liu Zeyu Liu Xinru Liu Pei Xian Univ Sci & Technol Sch Elect & Control Engn Xian 710054 Shaanxi Peoples R China State Grid BaoDing Elect Power Supply Co Baoding 071000 Peoples R China

ISBN: (纸本)9781538667750

Difficulties in the measurement of cable hybrid transmission lines are mainly due to the large differences in the structure and characteristics of cables and overhead lines. The wave speed and wave impedance of the two are different. When the fault traveling wave propagates to the connection point of the two, a strong wave deflection phenomenon will occur, which causes the traditional traveling wave ranging error to increase, and various single-ended traveling wave fault location algorithms cannot be directly applied to the overhead line-Cable hybrid line. Based on the multi-scale wavelet decomposition of signals and combined with the clustering analysis method, a new fault location method based on k-means algorithm is proposed. For a high-voltage cable hybrid transmission line, a simulation model is established. The single-ended distance measurement method is used to simulate the experiment with different transition resistances. The ATP-EMTP simulation results show that the method improves the transmission. The accuracy and reliability of line fault location can provide reference for fault location of transmission lines.

关键词： cable overhead line k-means algorithm wavelet decompsoition cluster analysis fault location

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A Hybrid CRO-k-means algorithm for Data Clustering 1

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1st International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Panigrahi, Sibarama Rath, Balaram kumar, P. Santosh Natl Inst Sci & Technol Berhampur 761008 Odisha India MITS Engn Coll MIRC Lab Rayagada 765017 Odisha India

ISBN: (纸本)9788132222026;9788132222019

Over the past few decades clustering algorithms have been used in diversified fields of engineering and science. Out of various methods, k-means algorithm is one of the most popular clustering algorithms. However, k-means algorithm has a major drawback of trapping to local optima. Motivated by this, this paper attempts to hybridize Chemical Reaction Optimization (CRO) algorithm with k-means algorithm for data clustering. In this method k-means algorithm is used as an on-wall ineffective collision reaction in the CRO algorithm, thereby enjoying the intensification property of k-means algorithm and diversification of intermolecular reactions of CRO algorithm. The performance of the proposed methodology is evaluated by comparing the obtained results on four real world datasets with three other algorithms including k-means algorithm, CRO-based and differential evolution (DE) based clustering algorithm. Experimental result shows that the performance of proposed clustering algorithm is better than k-means, DE-based, CRO-based clustering algorithm on the datasets considered.

关键词： Data clustering Chemical reaction optimization k-means algorithm

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A new k-means algorithm for community structures detection based on Fuzzy clustering

A new K-means algorithm for community structures detection b...

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IEEE International Conference on Granular Computing (GrC)

作者： Liu, Qun Peng, Zhiming Gao, Yi Liu, Qian Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing Peoples R China

ISBN: (纸本)9781467323109;9781467323116

Finding community structures from networks is one of the most popular research areas in recent years. Because of the shortcoming of FCM, for example, its results depend on the initial center node and need to specify the community number, based on the fuzzy theory, an improved FCM algorithm(NkFCM) is proposed, which can get the number of communities and the community centers automatically. NkFCM is used to find the communities of network. The experiments in real networks show that this method can get better results.

关键词： Social network k-means algorithm Community structure Fuzzy C-means clustering

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Object Clustering by k-means algorithm with Binary Sketch Templates 4

Object Clustering by K-means Algorithm with Binary Sketch Te...

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4th IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Mei, Xueyan Columbia Univ Sch Profess Studies New York NY 10027 USA

ISBN: (纸本)9781509034840

Object clustering is a very challenging unsupervised learning problem in machine learning and pattern recognition. In this paper, we will study visual object pattern clustering problem by combining the k-means clustering algorithm and the binary sketch templates, which quantify each image by a vector of indicators showing that a sketch at certain location, scale, and orientation exist or not. This representation is very simple and accounts for shape deformation of objects by local max pooling operations. Most importantly, such representations can be visualized by meaningful symbolic sketch templates. The experiment conducting on a small clustering dataset shows that the k-means with binary sketch templates for object clustering is very promising and the learned mixture of templates is also meaningful for understanding the results.

关键词： image clustering k-means algorithm binary sketch template

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Agricultural Robot Navigation Path Recognition Based on k-means algorithm for Large-Scale Image Segmentation 14

Agricultural Robot Navigation Path Recognition Based on K-me...

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14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Mao, Jiandong Cao, Zhen Wang, Hongyan Zhang, Bai Guo, Zhen Niu, Wenqi North Minzu Univ Sch Elect & Informat Engn Yinchun Peoples R China Western Elect Commerce Co Ltd Yinchun Peoples R China

ISBN: (纸本)9781538694909

In order to solve the problem of agricultural robot navigation path recognition in the uneven illumination and complex background environment which lead to the poor accuracy of navigation path, a clustering algorithm for image segmentation is used in this paper. By introducing the Lab color space and k-means algorithm, the k-means clustering process can be performed with large-scale segmentation of the region of interest in the image. After clustered twice, the image can separate the path information of the farmland from background. The navigation path can be fitted by using the linear least squares method. For illustration, an image of the medlar farmland line is utilized to show the feasibility of this method. Experience results show that the method of clustering and segmenting the region of interest based on k-means algorithm can effectively improve the accuracy of image segmentation and solve the influence of uneven illumination and complex background environment on farmland navigation path accuracy.

关键词： Agricultural robot Lab color space k-means algorithm

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Implementation of Two Stages k-means algorithm to Apply a Payment System Provider Framework in Banking Systems 4th

Implementation of Two Stages <i>k</i>-Means Algorithm to App...

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4th Computer Science On-line Conference (CSOC)

作者： Ebadati, Omid Mahdi E. Babaie, Sara Sadat Univ Econ Sci Dept Math & Comp Sci Qarani & Vila 242 Somayeh St Tehran Iran Univ Econ Sci Dept Knowledge Engn & Decis Sci Tehran Iran

ISBN: (纸本)9783319184760;9783319184753

Payment Systems Providers (PSPs) are companies, which provide services of payment for their customers. Recently, according to some changes in Iran central bank rules, providing services of payment are not monitored by banks anymore. This duty is assigned to some organizations called PSPs and becomes one of the most challenging topics for them. Clustering the datasets, assessment and the way of expressing customers' demands and the provinces of requests should be recognized for improving services to the customers, banks, financial and credit institutes. The proposed framework consists of two stages using k-means algorithm and Euclidean square distances. The k-means algorithm is applied in the first stage for five provinces, which have the highest demands. In the second stage, the mean of centroids obtained from k-means are calculated and repeat clustering according to the minimum Euclidean square distances to the new centroids then comparing the information gained by two stages.

关键词： Payment Systems Providers Data mining k-means algorithm Artificial Intelligence in Bank Systems Euclidean distances

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Clustering with Spectral Norm and the k-means algorithm

Clustering with Spectral Norm and the <i>k</i>-means Algorit...

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IEEE 51st Annual Symposium on Foundations of Computer Science (FOCS)

作者： kumar, Amit kannan, Ravindran IIT Delhi Dept Comp Sci & Engg New Delhi India Microsoft Res Bengaluru India

ISBN: (纸本)9780769542447

There has been much progress on efficient algorithms for clustering data points generated by a mixture of k probability distributions under the assumption that the means of the distributions are well-separated, i.e., the distance between the means of any two distributions is at least Omega(k) standard deviations. These results generally make heavy use of the generative model and particular properties of the distributions. In this paper, we show that a simple clustering algorithm works without assuming any generative (probabilistic) model. Our only assumption is what we call a "proximity condition": the projection of any data point onto the line joining its cluster center to any other cluster center is Omega(k) standard deviations closer to its own center than the other center. Here the notion of standard deviations is based on the spectral norm of the matrix whose rows represent the difference between a point and the mean of the cluster to which it belongs. We show that in the generative models studied, our proximity condition is satisfied and so we are able to derive most known results for generative models as corollaries of our main result. We also prove some new results for generative models - e.g., we can cluster all but a small fraction of points only assuming a bound on the variance. Our algorithm relies on the well known k-means algorithm, and along the way, we prove a result of independent interest - that the k-means algorithm converges to the "true centers" even in the presence of spurious points provided the initial (estimated) centers are close enough to the corresponding actual centers and all but a small fraction of the points satisfy the proximity condition. Finally, we present a new technique for boosting the ratio of inter-center separation to standard deviation. This allows us to prove results for learning certain mixture of distributions under weaker separation conditions.

关键词： cluster center clustering data points intercenter separation k probability distributions k-means algorithm pattern clustering probability simple clustering algorithm spectral norm

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