Speaker: Xinwei D
Lecture in Data Science: Adaptive Convex Clustering with Applications to Purchase Likelihood Prediction
Lecture Date: Monday, June 29
Lecture Time: 1-2PM EDT
Abstract: Clustering and prediction are commonly encountered in data science applications. In the pricing of customized products, it is challenging to accurately predict the purchase likelihood of potential customers for each personalized request. The heterogeneity of customers and their responses to the personalized products leads to very different purchase behavior. There is a great need to construct distinctive models for different data segments. In this talk, we present an adaptive convex clustering method to perform data segmentation and model fitting simultaneously. The proposed method segments data points using the fused penalty to account for the similarity in model structures. It ensures that the data points sharing the same model structure are grouped into the same segment. Accordingly, we develop an efficient algorithm for parameter estimation and study its consistency properties in estimation and clustering. The merits of the proposed method are illustrated by numerical examples and case studies. This is a joint work with several researchers at the T.J. IBM Watson Research Center.