A computation study on contextual self-organizing maps for subset data integration

As sensing and data collection capabilities have dramatically increased in recent years, many areas from medicine to entertainment to engineering have to rethink how products are designed, delivered and maintained. In engineering fields data is everywhere and its use as a decision aid, in the constant stream of tradeoff decisions, is critical to delivering more robust products and services accurately and efficiently. Thus, the need to develop intelligent methods to analyze and visualize large datasets, to enable human understanding, is critical. One method that has been proven effective in this endeavor is the self-organizing map (SOM). However, SOMs require substantial computational resources and time to train, making them impractical for large datasets or datasets that may be added to over time. If this issue could be overcome, this approach could be widely adopted. This thesis studies the concept of using a subset of data to represent the characteristics of a full data set via a SOM. The correlation of a subset and full dataset SOM was studied on two different test cases. The percent difference of node weights was used to compare map representations between the partial and full datasets. A node alignment process was designed and implemented to enable a more accurate comparison of two SOMs. The methodology was evaluated on two test cases. A hundred comparisons of node weights from subset and full datasets maps were completed per test case. Results showed that pairing node weights by row and column designation did not accurately compare two different SOMs. The alignment process was then performed on ten samples of map comparisons per test case. Results of the aligned nodes provided a much more accurate comparison of SOMs from partial and full datasets. The results of this study show that with a good representative subset of data very similar nodal weights can be reached through map training compared to using the full dataset. This

[1]  T. Wilusz.  Neural networks — A comprehensive foundation: By Simon Haykin. Macmillan, pp. 696, ISBN 0-02-352761-7, 1994 , 1995 .

[2]  Miroslav Kubat,et al.  Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. , 1999, The Knowledge Engineering Review.

[3]  Mehmet Fidan,et al.  Sound based induction motor fault diagnosis using Kohonen self-organizing map , 2014 .

[4]  Mikko Kolehmainen,et al.  Evaluating online data of water quality changes in a pilot drinking water distribution system with multivariate data exploration methods. , 2008, Water research.

[5]  Dong-Gyun Hong,et al.  Stream modification patterns in a river basin: Field survey and self-organizing map (SOM) application , 2010, Ecol. Informatics.

[6]  Cherif Salama,et al.  Optimizing Self-Organizing Maps Parameters Using Genetic Algorithm: A Simple Case Study , 2019, AISI.

[7]  Milos Manic,et al.  Parallalizable deep self-organizing maps for image classification , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[8]  C. Douligeris,et al.  Detecting denial of service attacks using emergent self-organizing maps , 2005, Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005..

[9]  Chathurika S. Wickramasinghe,et al.  Deep Self-Organizing Maps for Unsupervised Image Classification , 2019, IEEE Transactions on Industrial Informatics.

[10]  Andreas Rauber,et al.  Analytic Comparison of Self-Organising Maps , 2009, WSOM.

[11]  Stefan Rüping,et al.  Analysis of IC fabrication processes using self-organizing maps , 1999 .

[12]  Trevor Richardson,et al.  A software environment for visualizing high-dimensional data using contextual self-organizing maps linked with immersive virtual reality , 2013 .

[13]  Alfred Ultsch,et al.  U *-Matrix : a Tool to visualize Clusters in high dimensional Data , 2004 .

[14]  Ganesh S. Oak.  Information Visualization Introduction , 2022 .

[15]  Ying Wei,et al.  Data-driven bearing fault identification using improved hidden Markov model and self-organizing map , 2018, Comput. Ind. Eng..

[16]  Eliot Winer,et al.  Visualizing engineering design data using a modified two-level self-organizing map clustering approach , 2019 .

[17]  B. Nekolny,et al.  Contextual self-organizing maps for visual design space exploration , 2010 .

[18]  Ali Selamat,et al.  An empirical study based on semi-supervised hybrid self-organizing map for software fault prediction , 2015, Knowl. Based Syst..

[19]  K. Yin,et al.  Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine , 2017 .

[20]  T. Kohonen,et al.  Visual Explorations in Finance with Self-Organizing Maps , 1998 .

[21]  Alexander N. Gorban,et al.  SOM: Stochastic initialization versus principal components , 2016, Inf. Sci..

[22]  J. H. Espina-Hernandez,et al.  Unwrapping the influence of multiple parameters on the Magnetic Barkhausen Noise signal using self-organizing maps , 2013 .

[23]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[24]  E. Pampalk.  Aligned Self-Organizing Maps , 2003 .

[25]  Bernardete Ribeiro,et al.  Clustering and visualization of bankruptcy trajectory using self-organizing map , 2013, Expert Syst. Appl..

[26]  Eliot Winer,et al.  Increasing Feasibility of the Self-Organizing Map as a Design Tool through a Novel Convergence Heuristic , 2015 .

[27]  Teuvo Kohonen,et al.  Exploration of very large databases by self-organizing maps , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[28]  Eliot Winer,et al.  Improving Contextual Self-Organizing Map Solution Times Using GPU Parallel Training , 2014 .

[29]  Linkan Bian,et al.  Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts , 2018 .

[30]  Scott A. Sisson,et al.  Tools for enhancing the application of self-organizing maps in water resources research and engineering , 2020 .

[31]  Shigeru Obayashi,et al.  Self-organizing map of Pareto solutions obtained from multiobjective supersonic wing design , 2002 .

[32]  Christian Rehtanz,et al.  Electric Power System's Stability Assessment and Online-Provision of Control Actions Using Self-Organizing Maps , 2001, IWANN.