Fingerprinting　localization　is　to　estimate　a　mobile　terminal＇s　location　using　its　online　received　signal　strength　(RSS)　measurement　and　offline　RSS　database　originated　from　multiple　access　points　(APs).　Kernel-based　fingerprinting　localization　is　such　a　competitive　algorithm.　However,　all　training　data　need　to　be　considered　in　its　offline　model　learning　stage.　This　render　high　risks　for　overfitting.　To　alleviate　this,　we　suggest　to　apply　clustering　to　the　localization　region　of　interest　first　and　then　use　kernal-based　fingerprinting　localization　for　each　cluster.　A　byproduct　of　clustering　is　that　the　computational　load　for　each　cluster　is　also　significantly　reduced.　To　further　reduce　the　computational　load　within　each　cluster,　we　also　suggest　to　apply　principal　component　compression　to　the　raw　RSS　measurements　to　reduce　their　dimensionality.　The　rationale　for　applying　principal　component　compression　is　that　the　distributions　of　the　RSS　measurements　at　all　calibration　points　(CPs)　within　each　cluster　will　be　more　similar　after　clustering.　Performance　evaluation　using　both　simulated　data　and　real　data　show　that　the　extended　kernel-based　fingerprinting　localization　using　clustering　and　principal　component　compression　have　better　location　estimation　accuracy　and　less　computational　load.　©　2016　ISIF.