This　article　proposes　single-objective/multiobjective　cat　swarm　optimization　clustering　algorithms　for　data　partition.　The　proposed　methods　use　the　cat　swarm　to　search　the　optimal.　The　position　of　the　cat　tightly　associates　with　the　clustering　centers　and　is　updated　by　two　submodes:　the　seeking　mode　and　the　tracing　mode.　The　seeking　mode　uses　the　simulated　annealing　strategy　to　update　the　cat　position　at　a　probability.　Inspired　by　the　quantum　theories,　the　tracing　mode　adopts　the　quantum　model　to　update　the　cat　position　in　the　whole　solution　space.　First,　the　single-objective　method　is　proposed　and　adopts　the　cohesion　of　clustering　as　the　objective　function,　in　which　the　kernel　method　is　applied.　For　considering　more　objective　functions　to　reveal　diverse　aspects　of　data,　the　multiobjective　method　is　proposed　and　adopts　both　the　cohesion　and　the　connectivity　as　the　objective　functions.　The　Pareto　optimization　method　is　applied　to　balance　the　objectives.　In　the　experiments,　three　kinds　of　data　sets　are　used　to　examine　the　effectiveness　of　the　proposed　methods,　which　are　three　synthetic　data　sets,　four　data　sets　from　the　UCI　Machine　Learning　Repository,　and　a　field　data　set.　Experimental　results　verified　that　the　proposed　methods　perform　better　than　the　traditional　clustering　algorithms,　and　the　proposed　multiobjective　method　has　the　highest　accuracy.　Note　to　Practitioners-This　article　presents　single-objective/multiobjective　cat　swarm　optimization　clustering　analysis　methods　for　data　partition.　Through　automatically　extracting　meaningful　or　useful　classes,　clustering　analysis　could　help　the　practitioners　or　the　intelligent　devices　find　the　specific　meanings　of　data,　natural　data　structure,　the　data　relationships,　or　other　characteristics.　The　proposed　methods　use　the　cat　swarm　to　search　the　optimal　clustering　result.　One　or　more　criterion　functions　could　be　selected　as　the　optimization　objectives.　The　time　complexity　of　the　multiobjective　type　is　higher　than　that　of　the　single-objective　type.　Therefore,　in　the　industrial　field,　engineers　should　choose　the　number　of　the　optimization　objectives　based　on　the　actual　requirements.　The　proposed　methods　could　be　widely　used　into　industrial　applications　to　deal　with　complex　data　sets.　Future　research　could　consider　some　more　progressive　optimization　schemes　to　improve　the　effectiveness.