Weighted Clusterwise Linear Regression based on adaptive quadratic form distance

作者：

Highlights：

• A Weighted Clusterwise Regression to obtain homogeneous clusters.

• Objective function combining a kmeans-like and a minimum SSQ criteria.

• Based on adaptive quadratic form dissimilarity, in x-space.

• Automatic weighing of explanatory variables under six constraints types.

• Synthetic and real datasets corroborate the usefulness of the method.

摘要

•A Weighted Clusterwise Regression to obtain homogeneous clusters.•Objective function combining a kmeans-like and a minimum SSQ criteria.•Based on adaptive quadratic form dissimilarity, in x-space.•Automatic weighing of explanatory variables under six constraints types.•Synthetic and real datasets corroborate the usefulness of the method.

论文关键词：Clusterwise regression,Quadratic form distance,Adaptive distances,Clustering

论文评审过程：Received 4 April 2020, Revised 14 March 2021, Accepted 12 July 2021, Available online 17 July 2021, Version of Record 20 July 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115609