Clustering with biological visual models

Alma Rodríguez, Erik Cuevas, Daniel Zaldivar and Luis Castañeda

Physica A: Statistical Mechanics and its Applications, 2019, vol. 528, issue C

Abstract: Clustering involves the process of dividing a collection of abstract objects into a number of groups, which are integrated with similar elements. Several clustering methods have been introduced and developed in the literature with different performance levels. Among such approaches, density algorithms present the best advantages, since they are able to find clusters from a dataset under different scales, shapes, and densities without requiring the number of groups as input. On the other hand, there are processes that humans perform much better than deterministic approaches or computers. In fact, humans can visually cluster data exceptionally well without the necessity of any training. Under this unique capability, clustering, instantaneous and effortless for humans, represents a fundamental challenge for artificial intelligence. In this paper, we develop a simple clustering model inspired by the way in which the human visual system associates patterns spatially. The model, at some abstraction level, can be characterized as a density grouping strategy. The approach is based on Cellular Neural Networks (CNNs) which have demonstrated to be the best models for emulating the human visual system. In the proposed method, similar to the biological model, the CNN is used to build especially groups while an automatic mechanism tries different resolution scales to find the best possible data categorization. Different datasets have been adopted to evaluate the performance of the proposed algorithm. Their results are also compared with popular density clustering techniques from the literature. Computational results demonstrate that the proposed CNN approach presents competitive results in comparison with other algorithms regarding accuracy and robustness.

Keywords: Clustering; Cellular neural/nonlinear networks; Density methods; Unsupervised learning (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:528:y:2019:i:c:s0378437119308969

DOI: 10.1016/j.physa.2019.121505

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