Title

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

College

College of Computer Studies

Department/Unit

Computer Technology

Document Type

Article

Source Title

International Journal of Computational Intelligence and Applications

Volume

17

Issue

3

Publication Date

9-1-2018

Abstract

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.

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Digitial Object Identifier (DOI)

10.1142/S1469026818500141

Disciplines

Computer Sciences | Data Science

Keywords

Self-organizing maps; Information visualization; Data sets; Document clustering

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