User behavior analysis method based on feature binning and K-Means algorithm
To analyze the shopping behavior generated by online shopping users,firstly,a customer relationship man-agement model(RFM model)is constructed through data processing,and on the basis of this model,two methods of feature binning and K-Means clustering are used to classify users,and compare and analyze the results of the two models,discuss their differences and specific application scope and significance.Among them,the RFM model based on the feature binning method converts the variables to similar scales and discretizes the variables,so that the user classification labels are clearer,and different types of users can also be classified according to various labels.The K-Means algorithm evaluates the quality of the clustering algorithm by the silhouette coefficient so as to select the optimal K value.The experimental analysis results in this paper can provide operators with more reliable and intui-tive data,so that operators can segment the market according to the different behaviors of different users,and then conduct precise marketing and service settings.
万方数据
维普
