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

Journal Article


Abstract


  • © 2018 Elsevier Ltd 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.

Publication Date


  • 2018

Citation


  • Saraswati, A., Nguyen, T., Hagenbuchner, M. & Tsoi, A. (2018). High-resolution Self-Organizing Maps for advanced visualization and dimension reduction. Neural Networks, 105 166-184.

Scopus Eid


  • 2-s2.0-85047409283

Number Of Pages


  • 18

Start Page


  • 166

End Page


  • 184

Volume


  • 105

Place Of Publication


  • United Kingdom

Abstract


  • © 2018 Elsevier Ltd 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.

Publication Date


  • 2018

Citation


  • Saraswati, A., Nguyen, T., Hagenbuchner, M. & Tsoi, A. (2018). High-resolution Self-Organizing Maps for advanced visualization and dimension reduction. Neural Networks, 105 166-184.

Scopus Eid


  • 2-s2.0-85047409283

Number Of Pages


  • 18

Start Page


  • 166

End Page


  • 184

Volume


  • 105

Place Of Publication


  • United Kingdom