As　a　low-cost,　all-purpose　parallel　computing　system　with　the　advantages　of　using　easily　and　good　expandability,　the　Cluster　System　has　become　a　popular　platform　in　lots　of　fields.　Clustering　analyzing　is　one　of　the　important　problems　in　Data　Mining.　Because　most　of　its　objects　are　large-scale　databases　or　high-dimension　data,　clustering　requests　more　powerful　computing　ability.　So　how　to　develop　parallel　clustering　algorithm　based　on　Cluster　System　deserves　attention.　This　paper　proposes　a　new　parallel　clustering　algorithm　called　PARCLE　for　very　large　databases　that　is　suitable　for　Cluster　System.　This　algorithm　adopts　data　parallelism　and　asynchronous　communication　to　reduce　the　communication　costs.　It　applies　a　new　clustering　algorithm　derived　from　BIRCH[2]　to　improve　the　quality　of　clustering.　Our　implementation　shows　high　speedups　with　negligible　communication　overheads　and　good　clustering　result　not　less　than　that　of　linear　clustering　algorithm.