We applied the K-mean++ clustering algorithm to separate individual traces into distinct populations . Each trace was segmented into a 30-by-30 bin 2D histogram and a 100-bin 1D histogram. The resulting data, encompassing both conductance and displacement information, was then binned into these histograms. The 30-by-30 2D histogram was reshaped into vector containing 900 elements. Upon adding the 100 elements from the 1D histogram, a 1000element feature vector was generated, encapsulating displacement and conductance data. K-mean++ clustering algorithm was then performed on these feature vectors to effectively distinguish between the high and low conductance features.