四川轻化工大学学报（自然科学版）2025，Vol.38Issue(1)：77-85,9.DOI:10.11863/j.suse.2025.01.09
基于数据挖掘的川菜食谱数据多维度可视分析
Multi-dimensional Visual Analysis of Sichuan Cuisine Recipe Data Based on Data Mining
摘要
Abstract
In view of the fact that the research on Sichuan cuisine pays less attention to multi-dimensional data analysis such as flavor,cooking techniques and time-consuming,a multi-dimensional visual analysis method of Sichuan cuisine recipe data based on data mining is proposed.Firstly,the sunburst map is used to express the multi-dimensional attributes of Sichuan cuisine,and the composition characteristics of Sichuan cuisine are analyzed.Secondly,the clustering analysis of Sichuan cuisine recipes is carried out,and by optimizing the selection of the initial clustering centers,the variance ratio criterion index and contour coefficient are obtained to be the largest(the largest volues are 3.657 and 0.359,respectively),and the clustering effect is better,which solves the problem of unstable clustering results of the traditional K-means algorithm.Then,association rules is used to analyze the common collocation of recipe ingredients.Finally,the dishes are weighted by the TF-IDF algorithm and the cosine similarity is calculated.The analysis results show that the fat content of Sichuan cuisine is high,the proportion of animal raw materials in the main ingredients is 71.00%,the spicy and salty umami are the most significant flavor types,and the more shared ingredients,the higher the similarity.The proposed method can analyze the composition characteristics of Sichuan cuisine and the association between recipes from multiple dimensions,helping users to discover the internal characteristics and associations of Sichuan cuisine recipes.关键词
数据挖掘/多维数据可视化/K-means算法/关联规则/TF-IDF算法Key words
data mining/multidimensional data visualization/K-means algorithm/association rule/TF-IDF algorithm分类
信息技术与安全科学引用本文复制引用
李苗苗,华才健,赵长青..基于数据挖掘的川菜食谱数据多维度可视分析[J].四川轻化工大学学报（自然科学版）,2025,38(1):77-85,9.基金项目
四川省科技厅项目(2019YFG0169) （2019YFG0169）
四川轻化工大学研究生创新基金项目(Y2023118) （Y2023118）
