A fuzzy kernel ??-means clustering algorithm(FKC) is proposed to resolve the location fingerprint(LF) clustering.
LF is summarized as a kind of interval-valued data which obey normal distribution to describe sampling uncertainty
of received signal strength of access point. After mapping LF into the high-dimensional feature space through normal
distribution function determined by interval median and size, LF is clustered with fuzzy c-means algorithm based on kernel
method in the feature space. Results of ZigBee positioning experiments show that FKC can get better clustering effect than
c-means algorithm based on the average value of signal strength. On the premise of ensuring the positioning precision, a
feasible solution is provided to decrease the positioning calculation consumption remarkably.