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The Research on Artificial Bee Colony Algorithm for Fuzzy Clustering of Data Mining

Author: ZhangShouMing
Tutor: ZhaoXiaoQiang
School: Lanzhou University of Technology
Course: Control Theory and Control Engineering
Keywords: Data Mining Artificial bee colony Fuzzy C-Means Clustering The nuclear ambiguity C- means clustering The Boltzman Select mechanism TE process Popular learning
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 194
Quote: 0
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Abstract


As social and economic progress, the industrial production process increasingly high degree of automation and intelligent. The deepening of the degree of automation to enable enterprises to accumulate and store more and more of the process of historical data. With the continuous improvement of the enterprise to its own requirements, on the one hand, the rich data resources become available resources; On the other hand, the production practices of the industrial process and scientific research again based on large amounts of data need to pass some of the methods and online means of analysis, processing, industrial process monitoring, process identification, fault diagnosis and control strategy design. Therefore, data mining as a technical means to extract useful information from large data by more and more attention. Artificial bee colony algorithm is a simulation of the search behavior of bees swarm intelligence swarm intelligence optimization algorithm. Due to its control parameters, easy to implement, simple calculation, etc., have been concerned by the growing number of scholars. Fuzzy C-means clustering (FCM) and nuclear fuzzy C-means clustering (KFCM) algorithm has been applied to pattern recognition, image processing and computer vision, and many other areas, but there are still some drawbacks. In this paper, the FCM algorithm easy to fall into local minimum value and sensitive to the initial value of the shortcomings, a fuzzy clustering algorithm based on artificial bee colony (ABC). The KFCM algorithm to overcome a certain extent, the dependence of the internal shape of the data distribution, but there are still sensitive to initial value, easy to fall into local minimum value of the shortcomings. To this end, this paper presents a fuzzy C-means clustering algorithm (ABC-KFCM) based on artificial bee colony nuclear. The ABFM algorithm and the ABC-KFCM algorithm effectively improve search efficiency and reduce the search process into a local optimum phenomenon, but the effect is not very good in Japanese dimension of the number of clusters, for the introduction of the the Boltzmann selection mechanism in place roulette The choice of gambling, and more capable of inter-cell to generate the initial population by homogenization, the global optimization of the algorithm. Experimental results show that the new algorithm not only overcome the FCM and KFCM algorithm easy to fall into the local optimum drawback, but also for the clustering effect of the relatively large dimensions of the number of clusters in the data sample is more accurate and efficient. TE process has a process variable data dimension, the complexity of the relationship between variables, the effect of the non-linear characteristics of traditional dimensionality reduction method can not be expected, therefore, the introduction of popular learning method to reduce the dimension of data, and then with clustering analysis ABFM algorithm. The simulation results show that the algorithm has improved the feasibility and superiority, and to achieve the desired effect.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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