An improved clustering method based on biological visual models

A clustering methodology based on biological visual models that imitates how humans visually cluster data by spatially associating patterns has been recently proposed. The method is based on Cellular Neural Networks and some resolution adjustments. The Cellular Neural Network rebuilds low-density areas while different resolutions find the best clustering option. The algorithm has demonstrated good performance compared to other clustering techniques. However, its main drawbacks correspond to its inability to operate with more than two-dimensional data sets and the computational time required for the resolution adjustment mechanism. This paper proposes a new version of this clustering methodology to solve such flaws. In the new approach, a pre-processing stage is incorporated featuring a Self-Organization Map that maps complex high-dimensional relations into a reduced lattice yet preserving the topological organization of the initial data set. This reduced representation is employed as the two-dimensional data set for further processing. In the new version, the resolution adjustment process is also accelerated through the use of an optimization method that combines the Hill-Climbing and the Random Search techniques. By incorporating such mechanisms rather than evaluating all possible resolutions, the optimization strategy finds the best resolution for a clustering problem by using a limited number of iterations. The proposed approach has been evaluated, considering several two-dimensional and high-dimensional datasets. Experimental evidence exhibits that the proposed algorithm performs the clustering task over complex problems delivering a 46% faster on average than the original method. The approach is also compared to other popular clustering techniques reported in the literature. Computational experiments demonstrate competitive results in comparison to other algorithms in terms of accuracy and robustness.