Spatial Projection Pursuit based on Multiobjective optimization

Data mining is a huge and an interesting domain of patterns extraction. Spatial clustering has been distinguished as a major data mining task. It consists of grouping comparable objects into classes while taking into account the spatial aspect. It plays an important role in different areas, it is why several techniques have been proposed. This article presents a new approach based on the search of spatial clusters using Projection Pursuit with a dual mode. The idea is to look for projections revealing clusters that take into account the spatial information contained in the data. This involves solving a bi-objective problem where the first objective function is a projection index dedicated to the search of clusters and the second objective is a distance function defined for this purpose. Accordingly, a Multiobjective bio-inspired algorithm is used. Combining the spatial aspect with the Projection Pursuit and introducing a Multiobjective bio-inspired method in the same context is a first study in the literature. This new approach has been tested with real and simulated datasets, the experiments yield promising results.