Background:　In　clinical　practice,　longitudinal　data　can　be　used　to　find　trend　patterns　of　pathema　progress,　such　as　tumour　progress,　along　a　time　axis.　This　kind　of　data　can　be　treated　as　time-series　data.　The　maximum　common　sub-sequence　is　the　most　common　method　for　calculating　similarity　of　time-series　data;　and　each　point　is　normally　treated　as　having　the　same　weight.　However,　not　all　points　of　data　within　the　time　series　should　be　given　the　same　importance.　According　to　clinical　experience,　the　later　period　sub-sequence　(closer　to　death)　has　a　more　significant　effect　than　earlier　periods　in　a　trend　analysis.　Results:　A　weighted-similarity　measure　based　on　LCSS　with　Constraint　Window　(W-LCSS-CW)　Method　is　proposed.　The　results　obtained　from　the　time-series　data　using　different　weighting　factors　are　discussed.　In　a　study　of　non-small　cell　lung　cancer　using　time-series　data,　the　relative　evaluation　method　and　external　evaluation　method　were　adopted　to　calculate　cluster　effect.　The　results　show　that　the　proposed　method,　W-LCSS-CVV,　can　improve　clustering　performance　significantly.　Clustering　performance　of　various　methods　was　performed　using　a　comparison　of　(C-index/M-index).　The　proposed　W-LCSS-CW　Method　was　evaluated　to　1.55　which　was　37.02%,　48.01%,　49.64%　higher　than　other　common　methods　(Euclidean,　DTW,　STS)　respectively.　Conclusions:　The　proposed　W-LCSS-CW　Method　is　recommended　for　monitoring　time-series　data　of　tumour　patients　because　the　incorporated　weighting　factor　provides　more　convincing　cluster　results　for　medical　assist　support.　(C)　2014　Elsevier　Ltd.　All　rights　reserved.