Research On Elastic Net Algorithm For Clustering Based On Center Shift

In today's society,cluster analysis is one of the important ways for people to deal with various data mining problems.Neural networks have the advantages of self-learning and highspeed search for optimal solutions.The use of neural network algorithms to solve clustering problems has been a research hotspot in recent years.The elastic network algorithm(ENA)is a powerful neural network algorithm with the advantages of simple network structure and self-learning,but it is mainly used for traveling salesman problems and rarely used for solving clustering problems.This paper analyzes and studies the current research status of current clustering algorithms,neural networks and elastic network algorithms at home and abroad.Then the characteristics,advantages and disadvantages of the current main clustering methods are analyzed.Based on the characteristics of clustering problem,starting from two aspects of the number of clusters given and not given,clustering algorithms for clustering problems in two cases were proposed: the elastic network clustering algorithm based on center drift(CMENA)And adaptive elastic network clustering algorithm(ENACS)based on center drift.The CMENA algorithm adjusts and optimizes the structure of the elastic network based on the SED(Sum of European distances)value,one of the evaluation indicators of clustering,so that the energy function of the elastic network is minimized and the objective function value of the clustering problem is minimized Synchronize.Among them,the CMENA algorithm controls the movement of the central neurons in the cluster by minimizing the new energy function,and obtains the clustering result.It has the tracking of the clustering process,stable clustering results,strong anti-interference ability,significantly improved solution quality,and is suitable for solving.Advantages such as cluster analysis problems with high dimensions and large data volume.For the clustering problem where the number of clusters is not given,the ENACS algorithm combines the idea of Mean-Shift algorithm based on the CMENA algorithm,changes the given method of the number of initial neurons,and adds to the network structure of the CMENA algorithm.The network structure of the algorithm is optimized so that ENACS can perform clustering under unknown circumstances and obtain better clustering results.A large number of experiments have proved that the ENACS and CMENA algorithms have unique clustering results when clustering the same data set;ENACS and CMENA algorithms have the same number of clusters when clustering the same data set.Only,compared with other commonly used clustering algorithms,the clustering effect is significantly improved.