An adaptive mean shift clustering algorithm based on locality-sensitive hashing

The time complexity of the adaptive mean shift is related to the dimension of data and the number of iterations. The amount of computation will increase prohibitively with the increase of the data dimension. An approximate neighborhood queries method is presented for the computation of high dimensional data, in which, the locality-sensitive hashing (LSH) is used to reduce the computational complexity of the adaptive mean shift algorithm. The data-driven bandwidth selection for multivariate data is used in mean shift procedure, and an adaptive mean shift based on LSH with bandwidth estimation (LSH-PE-AMS) algorithm is proposed. Experimental results show that the proposed algorithm can reduce the complexity of the adaptive mean shift algorithm, and can produce a more accurate classification than the fixed bandwidth mean shift algorithm.