A multidimensional flocking algorithm for clustering spatial data

In this paper, we describe the efficient implementation of M-Sparrow, an adaptive flocking algorithm based on the biology-inspired paradigm of a flock of birds. We extended the classical flock model of Reynolds with two new characteristics: the movement in a multi-dimensional space and different kinds of birds. The birds, in this context, are used to discovery point having some desired characteristics in a multidimensional space. A critical point of the algorithm is the efficient search of the k-neighbors in a multidimensional space. This search was efficiently implemented using the ANN libraries.