Deep　clustering　aims　to　promote　clustering　tasks　by　combining　deep　learning　and　clustering　together　to　learn　the　clustering-oriented　representation,　and　many　approaches　have　shown　their　validity.　How-ever,　the　feature　learning　modules　in　existing　methods　hardly　learn　a　discriminative　representation.　In　addition,　the　label　assignment　mechanism　becomes　inefficient　when　dealing　with　some　hard　samples.　To　address　these　issues,　a　new　joint　optimization　clustering　framework　is　proposed　through　introducing　the　contractive　representation　in　feature　learning　and　utilizing　focal　loss　in　the　clustering　layer.　The　con-tractive　penalty　term　added　in　feature　learning　would　cause　the　local　feature　space　contraction,　resulting　in　learning　more　discriminative　features.　To　our　certain　knowledge,　this　is　also　the　first　work　to　utilize　the　focal　loss　to　improve　the　label　assignment　in　deep　clustering　method.　Moreover,　the　construction　of　the　joint　optimization　framework　enables　the　proposed　method　to　learn　feature　representation　and　la　-bel　assignment　simultaneously　in　an　end-to-end　way.　Finally,　we　comprehensively　compare　with　some　state-of-the-art　clustering　approaches　on　several　clustering　tasks　to　demonstrate　the　effectiveness　of　the　proposed　method.　(c)　2021　Elsevier　Ltd.　All　rights　reserved.