Dissertation > Excellent graduate degree dissertation topics show
Research and Improvement on KMeans Clustering Algorithm
Author: OuChenWei
Tutor: ChenZuo
School: Changsha University of Science and Technology
Course: Applied Computer Technology
Keywords: Clustering algorithms Kmeans algorithm Differential evolution algorithm
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 124
Quote: 0
Read: Download Dissertation
Abstract
With the rapid development of computer technology, people face all kinds of data, such as text data, image data, audio data, video data and so on. The quantity of these kinds of data is very large. How to quickly and effectively gain implicit and valuable information from these mass data has been a problem that has got much attention and should been solved urgently. Data mining (DM) has appeared in this situation. It has provided lots of efficient methods and tools on solving that problem for people. The Clustering analysis is one important method of them. It is an important part of data mining. With the gradually intensive research on clustering analysis these years, its importance has been recognized by people more and more. Clustering analysis technology has gained plentiful and substantial achievements in both theory and practice during recent years. At present, clustering analysis has been widely applied in machine learning, pattern recognition, image processing, text classification, marketing, statistical science and lots of others fields.According to the difference of data type, clustering purpose and application, we can divide existing clustering algorithms into partition algorithm, hierarchical algorithm, gridbased algorithm, densitybased algorithm and modelbased algorithm. One of the most mature and classical clustering algorithms is kmeans clustering algorithm. It is a partition algorithm. This paper presents deeply research and analysis on merits and defects of kmeans clustering algorithm. This paper has provided a improvement on kmeans clustering algorithm according to the feature that the results of kmeans clustering algorithm liable to be effected by initial centers. Following are the main works have been done:1. According to the defect that Kmeans clustering algorithm is dependent on the initial clustering centers selection, this paper put forward a new initial clustering centers selection method of kmeans algorithm. The experiments showed that this method has effectively solved the problem that the clustering result is always unstable due to the initial clustering centers overly close to each other and has improved effectiveness and stability of the clustering result.2. Aiming to the disadvantages of kmeans clustering algorithm that it is sensitive to the initial centers selection and easily falls into local optimal solution, differential evolution algorithm whose global optimization ability is strong was introduced into clustering in this paper. This paper put forward an improved differential evolution algorithm and made it combined with kmeans clustering algorithm at the same time. This method has solved initial centers optimization problem of kmeans clustering algorithm well. The experiments showed that the method has effectively improved clustering quality and convergence speed.

Related Dissertations
 Research on Scheduling of Wholeset Orders in JSP Based on Differential Evolution Algorithm,F273
 Research on Kmeans Optimization Clustering Algorithm,TP311.13
 Research on Fuzzy CMean Clustering Algorithm Based on Particle Swarm Optimization and Shuffled Frog Leaping Algorithm,TP18
 Evolutionary Clustering Algorithm and Its Application,TP311.13
 Web Usage Mining and the Research of Personalized Recommendation,TP311.13
 The Modified Harmony Search Algorithm with Control Parameters Coevolution and Its Application,TP391.3
 Library management system of personalized service Design and Implementation,TP311.52
 Modelbased rapid test method equipment,TJ06
 Subway construction project risk evaluation methods and criteria for research,U231.3
 Intelligent mobile robot map description and navigation methods,TP242.6
 Research and Implementation based the WebService execution management system,TP311.52
 Research on an Improved Clustering Algorithm of k_means,TP311.13
 Markov random field DS evidence theory of the human brain image segmentation,TP391.41
 MultiAgent Differential Evolution Algorithm and Its Applications in Optimization of Fermentation,TP18
 Research and Development of CustomerOriented Quick Quote System for LowVoltage Products,F426.63
 Research on Dynamic Decoupling for MultiAxis Sensor,TP212
 Study of Spatial and Temporal Dimensions Dynamical System Modeling Based on Multipolymerization Process Neural Networks,TP391.9
 Research of Parametric Method on Electrical Impedance Endotomography,R318.0
 Study on Attribute Reduction Method Based on Evolutionary Algorithm,TP18
 Application in Campus Network of Intrusion on Detection System Based on Data Mining,TP393.08
CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
© 2012 www.DissertationTopic.Net Mobile
