Fusion of evolvable genome structure and multi-objective optimization for subspace clustering

Highlights：

• This paper reports the first attempt in integrating multiobjective opti- mization (MOO) and genomic structure for solving the subspace clustering problem.

• Multiobjective optimization framework is utilized to simultaneously opti- mize several sub-space cluster quality measures.

• The existing cluster quality measures are modified to handle subspace clustering problem.

• As a part of the experiments, the proposed algorithm is applied on the bi-clustering of gene expression data to show the efficacy of the existing technique in solving some real-life problem. Bi-clustering of gene expres- sion data is a sub-space clustering problem where a subset of rows and a subset of columns need to be selected.