An algorithm for discovering clusters of different densities or shapes in noisy data sets
Fereshte Khani, Mohmmad Javad Hosseini, Ahmad Ali Abin, Hamid Beigy · 2013
In clustering spatial data, we are given a set of points in Rn and the objective is to find the clusters (representing spatial objects) in the set of points. Finding clusters with different shapes, sizes, and densities in data with noise and potentially outliers is a challenging task. This problem is especially studied in machine learning community and has lots of applications. We present a novel clustering technique, which can solve mentioned issues considerably. In the proposed algorithm, we let the structure of the data set itself find the clusters, this is done by having points actively send and receive feedbacks to each other.