基于RFM模型的半监督聚类算法

Semi-Supervised Clustering Algorithm Based on RFM Model

客户分类作为客户关系管理(CRM)的重要管理方法,是企业进行市场营销的重要依据.通过对客户进行分类,有利于对客户价值进行准确评估,方便进行精准营销.本文通过对RFM模型数据集本身潜藏的先验结构化信息进行研究,标记出两组客户数据作为先验类别标记,进而得到两个初始聚类中心.基于传统K-means算法使用自适应方法确定K值和初始聚类中心.引入Must-link和Cannot-link两种约束将类别标记转换为成对约束信息,基于HMRF-KMeans成对约束,引入约束惩罚项和约束奖励项,实现对聚类引导和聚类结果的调整.使用改进的半监督聚类算法(RFM-SS-means)对标准数据集进行了测试,同时使用Food mart数据集对比了RFM-SS-means算法与传统K-means算法、two-steps算法的聚类效果.由实验结果可知,RFM-SS-means的CH系数最大,无需事先确定K值和初始聚类中心,聚类效果良好.

As an important management method of customer relationship management（CRM）, the customer classification is the basis for enterprises to carry out marketing. The classification of customers is conducive to accurate assessment of customer value and facilitate the precise marketing. In this paper, we study the priori structured information hidden in the RFM model dataset, and mark two sets of customer data as a priori category mark, and then get two initial clustering centers. Based on the traditional K-means algorithm, the K value and the initial clustering center are determined with the adaptive method. Combining the two types of constraints of Must-link and Cannot-link, the category markers are transformed into pairs of constraint information. Based on HMRF-KMeans pairs, the constraints and constraint bonuses are introduced to improve the clustering guidance and clustering results. The improved semi-supervised clustering algorithm（RFM-SS-means） was used to test the standard data set, and the Food mart data set was also used to compare the RFM-SS-means algorithm with the traditional K-means algorithm and the two-steps algorithm Class effect. From the experimental results, it can be seen that the CH coefficient of RFM-SS-means is the largest, and the clustering effect is good without prior determination of K value and initial clustering center.