Although　multi-objective　evolutionary　subspace　clustering　approaches　have　shown　promise　in　handling　high-dimensional　datasets,　their　performance　is　restricted　by　two　main　drawbacks.　First,　their　local　search　strategies　have　not　been　well　investigated.　Second,　while　exploring　the　search　space,　they　neglect　the　useful　knowledge　from　previously　solved　problems.　To　tackle　these　issues,　this　paper　proposes　a　transfer　learning-assisted　multi-objective　evolutionary　clustering　framework　with　decomposition.　Firstly,　we　provide　a　decomposition-based　local　search　strategy.　To　capture　a　comprehensive　data　structure,　this　strategy　updates　the　weights　of　features　by　considering　both　the　within-class　compactness　and　between-class　separation,　and　spontaneously　balances　the　two　properties.　Secondly,　we　develop　a　knowledge　transfer　strategy.　By　transferring　search　experience　from　a　previously　solved　clustering　problem,　the　strategy　improves　the　search　efficiency,　consequently　enhances　the　clustering　accuracy　of　the　current　problem.　It　has　a　closed-form　solution　and　can　transfer　knowledge　across　both　homogeneous　and　heterogeneous　problems　from　either　different　or　the　same　domains.　Finally,　we　conduct　an　extensive　experimental　study　on　the　framework　by　comparing　with　six　representative　subspace　clustering　approaches　on　a　wide　range　of　benchmarks　and　real-world　applications.　Results　demonstrate　the　superiority　of　our　framework.　(C)　2019　Elsevier　Inc.　All　rights　reserved.