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检索条件"主题词=K-means algorithm"

共 1243 条记录，以下是81-90 订阅

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A Novel Genetic algorithm Based k-means algorithm for Cluster Analysis 3rd

A Novel Genetic Algorithm Based <i>k</i>-means Algorithm for...

引用

3rd International Conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： El-Shorbagy, M. A. Ayoub, A. Y. El-Desoky, I. M. Mousa, A. A. Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Taif Univ Fac Sci Math & Stat Dept At Taif Saudi Arabia

ISBN: (纸本)9783319746906;9783319746890

This paper proposed a novel genetic algorithm (GA) based k-means algorithm to perform cluster analysis. In the proposed approach, the population of GA is initialized by k-means algorithm. Then, the GA operators are applied to generate a new population. In addition, new mutation is proposed depending on the extreme points of clustering. The proposed approach is applied on a set of test problems. The results proved the superiority of the new methodology to perform cluster analysis well.

关键词： Cluster analysis k-means algorithm Genetic algorithm

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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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An Improved k-means algorithm for Clustering Using Entropy Weighting Measures

An Improved k-means Algorithm for Clustering Using Entropy W...

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7th World Congress on Intelligent Control and Automation

作者： Li, Taoying Chen, Yan Dalian Maritime Univ Sch Econ & Management Dalian 116026 Peoples R China

ISBN: (纸本)9781424421138

The objective of traditional k-means algorithm is to make the distances of objects in the same cluster as small as possible, but another objective that the distances of objects from different clusters is not taken into account. This paper presents an improved k-means algorithm satisfying both of objectives above. We modify the cost function of entropy weighting k-means clustering algorithm by adding a variable that is relevant linearly to the square sum of distances from the mean of all objects and the means of all dusters. The improved k-means clustering algorithm is presented and the effectiveness of the algorithm is demonstrated by comparing the results with other k-means clustering algorithms on Iris data.

关键词： clustering entropy weighting k-means algorithm partition clustering

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The SkM algorithm: A k-means algorithm for Clustering Sequential Data

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11th Ibero-American Conference on Artificial Intelligence

作者： Dias, Jose G. Cortinhal, Maria Joao ISCTE Business Sch Dept Quantitat Methods P-1649026 Lisbon Portugal

ISBN: (纸本)9783540883081

This paper introduces a new algorithm for clustering sequential data. The SkM algorithm is a k-means-type algorithm suited for identifying groups of objects with similar trajectories and dynamics. We provide a simulation study to show the good properties of the SkM algorithm. Moreover, a real application to website users' search patterns shows its usefulness in identifying groups with heterogeneous behavior. We identify two distinct clusters with different styles of website search.

关键词： clustering sequential data k-means algorithm kL distance

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A Density-Based Method for Selection of the Initial Clustering Centers of k-means algorithm 2

A Density-Based Method for Selection of the Initial Clusteri...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Xin Du Ning Xu Cailan Zhou Shihui Xiao School of Computer Science and Technology Wuhan University of Technology School of Information Engineering Wuhan University of Technology

The initial clustering centers of traditional k-means algorithm are randomly generated from a data set,clustering effect is not very stable. Aimed at this problem, this paper puts forward a kind of op

ISBN: (纸本)9781467389808

关键词： Initial Clustering Centers k-means algorithm Local Density

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Research on improved RFM customer segmentation model based on k-means algorithm 5

Research on improved RFM customer segmentation model based o...

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5th International Conference on Computational Intelligence and Applications (ICCIA)

作者： Huang, Yong Zhang, Mingzhen He, Yue Sichuan Univ Business Sch Chengdu Peoples R China

ISBN: (纸本)9781728160429

The RFM model used for customer segmentation in the traditional retail industry is not suitable for the industry with distinct attributes of social groups, so the RFMC model is created by introducing the parameter C of social relations. Educational e-commerce enterprise M is selected for empirical study, and k-means algorithm is used for cluster analysis of valid customers of enterprise M, which resulted in 5 distinct customer groups and verified the effectiveness of the model.

关键词： k-means algorithm Improved RFM model customer segmentation Educational e-commerce

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PSO-based procedure to find number of clusters and better initial centroids for k-means algorithm: Image segmentation as case study 6

PSO-based procedure to find number of clusters and better in...

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6th International Conference on Pattern Recognition and Image Analysis (IPRIA)

作者： Zarei, M. Alizadeh Nickfarjam, A. M. Islamic Azad Univ Kashan Dept Comp Engn Fac Elect & Comp Engn Kashan Iran Kashan Univ Med Sci Dept Hlth Informat Management & Technol Kashan Iran Kashan Univ Med Sci Res Ctr Hlth Informat Management Kashan Iran

ISBN: (纸本)9798350314557

In this paper, we propose a combination of k-means algorithm and Particle Swarm Optimization (PSO) method. The k-means algorithm is utilized for data clustering. On one hand, the number of clusters (k) should be determined by expert or found by try-and-error procedure in the k-means algorithm. On the other hand, initial centroids and number of clusters (k) are influenced on the quality of resulted grouping. Therefore, the aim of the proposed procedure is using PSO and the Structural Similarity Index (SSIM) criterion as a fitness function in order to find the best value for k parameter and better initial clusters' center. Due to different value of k parameter, the number of initial centroids which should be produced is variant. Thus, length of particles in PSO method may be different in each iteration. Experimental results show the superiority of this approach in comparison with standard k-means algorithm and both of them are evaluated on image segmentation problem.

关键词： Data Clustering Particle Swarm Optimization k-means algorithm Image Segmentation Structural Similarity Index

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A Review on Outlier Detection Techniques on Data Stream by Using Different Approaches of k-means algorithm 2

A Review on Outlier Detection Techniques on Data Stream by U...

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International Conference on Advances in Computer Engineering and Applications (ICACEA)

作者： Chauhan, Prashant Shukla, Madhu Marwadi Educ Fdn Grp Inst Dept Comp Engn Rajkot 360003 Gujarat India

ISBN: (纸本)9781467369114

Data Stream mining has gained attraction from many researchers as there is need to mine large dataset which pose different challenges for researchers. Stream data is different compared to normal data as they are continuously produced from different applications which impose different challenges like massive, infinite, concept drift for processing. An object that does not obey the behavior of normal data object is called outliers. Outlier detection is used in different applications like fraud detection, intrusion detection, track environmental changes, medical diagnosis so there is need to detect outliers from data streams. Various approaches are used for outlier detection. Some of them use k-means algorithm for outlier detection in data streams which help to create a similar group or cluster of data points. Data stream clustering techniques are highly helpful to cluster similar data items in data streams and also to detect the outliers from them, so they are called cluster based outlier detection. k-means algorithm is partition based algorithm which is used for clustering datasets into number of clusters. It is most common and popular algorithm for clustering due to its simplicity and efficiency. Purpose of this paper is to review of different approaches of outlier detection which is used for k-means algorithm for clustering dataset with some other methods. Different application areas of outlier detection are discussed in this paper.

关键词： data stream k-means algorithm outlier detection algorithm in data stream application area of outlier detection

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Identification of Artifacts in Scenery Images using Color and Line Information by RBF Network - Segmentation by k-means algorithm and Edge Information

Identification of Artifacts in Scenery Images using Color an...

引用

IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Sakashita, Yuta Osana, Yuko Tokyo Univ Technol Hachioji Tokyo Japan

ISBN: (纸本)9781457706530

In this paper, we propose a new image segmentation method based on k-means algorithm and edge information, and identification of artifacts in scenery images using color and line information by Radial Basis Function (RBF) network is realized. In the proposed method, the original image is divided into some areas based on color information by the k-means algorithm and edge information, and whether or not artifacts are included for each divided area are judged using color and line information by the RBF network. We carried out a series of computer experiments and confirmed that the proposed method can identify the area including artifacts with considerable accuracy.

关键词： Color k-means algorithm Nickel RBF network artifacts identification color image processing edge detection edge information image classification image classification image colour analysis image segmentation image segmentation line information pattern clust

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Evaluation of Class Decomposition based on Clustering Validity and k-means algorithm 21

Evaluation of Class Decomposition based on Clustering Validi...

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21st International Arab Conference on Information Technology (ACIT)

作者： Sowan, Bilal Matar, Nasim Omar, Firas Alauthman, Mohammad Eshtay, Mohammed Univ Petra Dept Business Intelligence & Data Analyt Amman Jordan Univ Petra Dept E Business & Commerce Amman Jordan Univ Petra Dept Informat Secur Amman Jordan LTUC Dept ASAC Amman Jordan

ISBN: (纸本)9781728188553

A class decomposition is one of the possible solutions and the most important factors of success for the improvement of classification performance. The idea is to transform a dataset by categorizing each class label into groups or clusters. Thus, the transformation is done concerning data characteristics and similarities. This paper proposed a hybrid model for a class decomposition by the integration of gap statistic, k-means clustering algorithm, and Naive Bayes classifier. The model is based on clustering validity using gap statistic for enhancing the classifier performance. The model works by dividing each dataset into several subsets regarding its class labels. After that, the clustering validity using gap statistic is employed for estimating the optimal number of clusters for each subset that belong to a particular class label. The estimated number of clusters is used then as an input parameter for the k-means clustering algorithm for relabeling the data objects with a new class label in each subset. Every data object is allocated to each of the clusters generated by the k-means clustering algorithm, which consider it as the new class label. The proposed model integrates the class decomposition approach with Naive Bayes classifier to compare the performance of the proposed model under several classification measures. The model is validated and evaluated by employing different real-world datasets collected from the UCI machine learning repository. The experimental results show that a significant improvement in classification accuracy and F-measure when the class decomposition is applied. Also, the experiments indicate that using a class decomposition is not appropriate for all datasets.

关键词： Class Decomposition Clustering Validity k-means algorithm Classification Naive Bayes

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