Improved subspace clustering algorithm using multi-objective framework and subspace optimization

作者：

Highlights：

• Integrates multi-objective in an evolutionary-based technique for subspace clustering.

• A first attempt in simultaneously optimizing the subspace feature set and compactness.

• A new validity index, PSM-index designed to optimizing the subspace feature sets.

• A modified and a new mutation operator is developed to explore the search space.

• Application of the proposed method is shown in bi-clustering the gene expression data.

摘要

•Integrates multi-objective in an evolutionary-based technique for subspace clustering.•A first attempt in simultaneously optimizing the subspace feature set and compactness.•A new validity index, PSM-index designed to optimizing the subspace feature sets.•A modified and a new mutation operator is developed to explore the search space.•Application of the proposed method is shown in bi-clustering the gene expression data.

论文关键词：Subspace clustering,Multi-objective Optimization (MOO),Intra-Cluster Compactness (ICC),Feature Non-Redundancy (FNR),Feature Per Cluster (FPC)

论文评审过程：Received 6 February 2020, Revised 18 April 2020, Accepted 26 April 2020, Available online 11 May 2020, Version of Record 26 May 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113487