关键词: 铝生产, 模糊聚类, 朴素贝叶斯, 累积法, 增量思想

Abstract: This paper presents a strategy to maintain the stability of aluminum reduction cells through the data mining and modeling of the aluminum factory in order to maintain the continuity of aluminum electrolytic production and ensure the stability of energy consumption and the stability of energy consumption. Firstly, de-noising for data. In view of the unknown data distribution characteristics of aluminum plants, a fuzzy clustering method without parameter adaptation is proposed, and the number and cluster centers of clusters can be obtained through iterative adaptation. According to the clustering results, the data of aluminum factory is labelled according to the actual meaning. A distance based continuous attribute naive Bias algorithm is proposed. The incremental idea of classifiers is used to improve the accuracy of algorithm classification. By using single slot test set data, every index level can be predicted by accumulating method, compared with the previous day, the change of variables are confirmed under each index, and then complete the prediction. It is found that the prediction model can predict the key indicators of aluminum electrolysis, and the proposed clustering and classification algorithms are good in UCI data and aluminum factory data.

Key words: aluminum production, fuzzy clustering, naive Bayes, cumulative method, incremental thinking