The　efficiency　of　clustering　algorithms　is　strongly　needed　with　very　large　databases　and　high-dimensional　data　types.　As　a　solution,　parallel　algorithms　can　be　used　to　provide　powerful　computing　ability.　PCs　cluster　system　is　one　of　low-cost　general-purpose　parallel　computing　systems.　In　this　paper,　we　first　theoretically　analyze　the　idea　of　adopting　data　parallelism　when　designing　a　parallel　clustering　algorithm　for　PCs　cluster　systems,　including　analysis　of　speedup　and　selection　of　communication　schemes.　We　then　present　a　parallel　hierarchical　clustering　algorithm　called　PARC.　Experiment　results　demonstrate　the　correctness　of　the　theoretical　analysis　and　show　that　in　general,　PARC　obtains　as　good　quality　of　clustering　as　linear　clustering　algorithms,　while　communication　time　is　considerably　improved.　(c)　2006　Elsevier　B.V.　All　rights　reserved.