Comparative Study on Feature Selection Approaches of Clustering Model Based on Machine Learning

摘要

Targeting at problems during the feature selection process of machine learning clustering model, this article firstly makes analysis on the applicability of the feature selection I clustering model and make adjustment and improvement, then makes feature selection algorithm design based on R language RFE feature selection method and Boruta feature selection method, and at last applies cluster interior validity indexes to analyze the optimization result of online brand loyalty clustering model for further comparative study on the feature selection approaches