Research on Classification Method of Highway Concrete Water Reducing Agent Manufacturers Based on K-means++ Clustering

Abstract

Concrete water reducing agent is an important admixture in the preparation of concrete. Machine learning has a wide range of applications in the field of material science. In this paper, machine learning is creatively applied to the classification of water reducing agent manufacturers. This is because the act of categorizing water reducing agent supply manufacturers in practical engineering relies heavily on the experience of experts, which makes it more difficult to find similar alternative manufacturers when the water reducing agent is out of stock. In order to solve this kind of problem, this paper first performs data cleaning on the original data, a total of 72-dimensional eigenvalues are selected, and missing value processing and standardization are performed to normalize the dataset. Afterwards, the K-means++ algorithm is used to analyze the clustering of manufacturers, and the optimal K value is selected by introducing three evaluation indexes, such as Silhouette Coefficient, and the optimal clustering effect is obtained when K is 3. At this time, when the manufacturers of a certain class are out of stock, similar manufacturers can be found for the replacement of the goods.