Robust Adaptive SOMs Challenges in a Varied Datasets Analytics

Abstract

The advancement of available technology in use cause the production of huge amounts of data which need to be categorised within an acceptable time for end users and decision makers to be able to make use of the data contents. Present unsupervised algorithms are not capable to process huge amounts of generated data in a short time. This increases the challenges posed by storing, analyzing, recognizing patterns, reducing the dimensionality and processing Data. Self-Organizing Map (SOM) is a specialized clustering technique that has been used in a wide range of applications to solve different problems. Unfortunately, it suffers from slow convergence and high steady-state error. The work presented in this paper is based on the recently proposed modified SOM technique introducing a Robust Adaptive learning approach to the SOM (RA-SOM). RA-SOM helps to overcome many of the current drawbacks of the conventional SOM and is able to efficiently outperform the SOM in obtaining the winner neuron in a lower learning process time. To verify the improved performance of the RA-SOM, it was compared against the performance of other versions of the SOM algorithm, namely GF-SOM, PLSOM, and PLSOM2. The test results proved that the RA-SOM algorithm outperformed the conventional SOM and the other algorithms in terms of the convergence rate, Quantization Error (QE), Topology Error (TE) preserving map using datasets of different sizes. The results also showed that RA-SOM maintained an efficient performance on all the different types of datasets used, while the other algorithms a more inconsistent performance, which means that their performance could be data type-related.