Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

作者：

Highlights：

• We present an integer linear programming (ILP) model for the categorical clustering.

• A global heuristic is presented for solving the ILP of medium and large size.

• Experiments show that the proposed method is superior to the existing approaches.

• New best results are provided for the UCI benchmark datasets.

摘要

•We present an integer linear programming (ILP) model for the categorical clustering.•A global heuristic is presented for solving the ILP of medium and large size.•Experiments show that the proposed method is superior to the existing approaches.•New best results are provided for the UCI benchmark datasets.

论文关键词：Categorical clustering,Variable neighborhood search,Data mining,Integer linear programming

论文评审过程：Received 20 April 2018, Revised 30 December 2018, Accepted 24 January 2019, Available online 29 January 2019, Version of Record 31 January 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.01.042