High-resolution Self-Organizing Maps for advanced visualization and dimension reduction

Neural Networks : the Official Journal of the International Neural Network Society
Ayu SaraswatiAh Chung Tsoi

Abstract

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected.

Citations

Sep 11, 2019·International Journal of Environmental Research and Public Health·Piotr BonieckiAlicja Kolasa-Wiecek
Jun 12, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Leonardo A DiasMarcelo A C Fernandes

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