A new method for emulating self-organizing maps for visualization of datasets

• Main Authors: Cordel, Macario O., II, Azcarraga, Arnulfo P.
• Format: text
• Published: Animo Repository 2018
• Subjects: Self-organizing maps; Information visualization; Data sets; Document clustering; Computer Sciences; Data Science
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2743
• Institution: De La Salle University

Several time-critical problems relying on large amount of data, e.g., business trends, disaster response and disease outbreak, require cost-effective, timely and accurate data summary and visualization, in order to come up with an efficient and effective decision. Self-organizing map (SOM) is a very effective data clustering and visualization tool as it provides intuitive display of data in lower-dimensional space. However, with O(N2) complexity, SOM becomes inappropriate for large datasets. In this paper, we propose a force-directed visualization method that emulates SOMs capability to display the data clusters with O(N) complexity. The main idea is to perform a force-directed fine-tuning of the 2D representation of data. To demonstrate the efficiency and the vast potential of the proposed method as a fast visualization tool, the methodology is used to do a 2D-projection of the MNIST handwritten digits dataset. © 2018 World Scientific Publishing Europe Ltd.