In　the　field　of　safety　and　communication　of　human-robot　interaction　(HRI),　using　large-scale　electronic　skin　will　be　the　tendency　in　the　future.　The　force-sensitive　piezoresistive　material　is　the　key　for　piezoresistive　electronic　skin.　In　this　paper,　a　non-array　large　scale　piezoresistive　tactile　sensor　and　its　corresponding　calibration　methods　were　presented.　Because　of　the　creep　inconsistency　of　large　scale　piezoresistive　material,　a　creep　tracking　compensation　method　based　on　K-means　clustering　and　fuzzy　pattern　recognition　was　proposed　to　improve　the　detection　accuracy.　With　the　compensated　data,　the　inconsistency　and　nonlinearity　of　the　sensor　was　calibrated.　The　calibration　process　was　divided　into　two　parts.　The　hierarchical　clustering　algorithm　was　utilized　firstly　to　classify　and　fuse　piezoresistive　property　of　different　regions　over　the　whole　sensor.　Then,　combining　the　position　information,　the　force　detection　model　was　constructed　by　Back-Propagation　(BP)　neural　network.　At　last,　a　novel　flexible　tactile　sensor　for　detecting　contact　position　and　force　was　designed　as　an　example　and　tested　after　being　calibrated.　The　experimental　results　showed　that　the　calibration　methods　proposed　were　effective　in　detecting　force,　and　the　detection　accuracy　was　improved.