Reconstructing Self Organizing Maps as Spider Graphs for better visual\n interpretation of large unstructured datasets

2012/12/24 by Aaditya Prakash, Prakash, Aaditya
Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Graphics (cs.GR) #Machine Learning (stat.ML) #Neural Networks and Applications #Neural Networks and Reservoir Computing

paper · pdf · doi:10.48550/arxiv.1301.0289

openalex publication_date 2012/12/24 · openalex created_date 2022/09/30 · openalex updated_date 2026/07/28

Abstract

Self-Organizing Maps (SOM) are popular unsupervised artificial neural network\nused to reduce dimensions and visualize data. Visual interpretation from\nSelf-Organizing Maps (SOM) has been limited due to grid approach of data\nrepresentation, which makes inter-scenario analysis impossible. The paper\nproposes a new way to structure SOM. This model reconstructs SOM to show\nstrength between variables as the threads of a cobweb and illuminate\ninter-scenario analysis. While Radar Graphs are very crude representation of\nspider web, this model uses more lively and realistic cobweb representation to\ntake into account the difference in strength and length of threads. This model\nallows for visualization of highly unstructured dataset with large number of\ndimensions, common in Bigdata sources.\n

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