Improved K-Means Clustering Algorithm Based on Dynamic Clustering

Abstract

Cluster analysis can not only find potential and valuable structured information in the data set, but also provide pre-processing functions for other data mining algorithms, and then can refine the processing results to improve the accuracy of the algorithm. Therefore, cluster analysis has become one of the hot research topics in the field of data mining. K-means algorithm, as a clustering algorithm based on the partitioning idea, can compare the differences between the data set classes and classes. We can use the K-means algorithm to mine the clustering results and further discover the potentially valuable knowledge in the data set. Help people make more accurate decisions. This paper summarizes and analyzes the traditional K-means algorithm, summarizes the improvement direction of the K-means algorithm, fully considers the dynamic change of information in the K-means clustering process, and reduces the standard setting value for the termination condition of the algorithm to reduce The number of iterations of the algorithm reduces the learning time; the redundant information generated by the dynamic change of information is deleted to reduce the interference in the dynamic clustering process, so that the algorithm achieves a more accurate and efficient clustering effect. Experimental results show that when the amount of data is large, compared with the traditional K-means algorithm, the improved K-means algorithm has a greater improvement in accuracy and execution efficiency. 1