Optimization Analysis of Clustering Algorithm under the Background of Big Data Data Mining

The computing technology developed from statistics has also become an important research content in the field of data mining. The purpose of this paper is to analyze the optimization of aggregation algorithms in the context of big data mining. Aiming at the loading problem in the context of big data, several aggregation algorithms are studied and discussed. First, the related concepts of the AMOSA algorithm are demonstrated, and the advanced AMOSA_FCM aggregation system is proposed on AMOSA, which improves the efficiency of the algorithm. For the problem of the next stage of the algorithm, a method of changing the local radius is proposed, which increases the ability to jump out of the local deficit. Experiments show that when the number of cluster centers is 2, the objective function value of the AMOSA_FCM algorithm is 9.89×10
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. The improved algorithm improves the performance of clustering and shows certain advantages compared with other algorithms.