Networking Self-Organising Maps and Similarity Weight Associations

Younjin Chung*, Joachim Gudmundsson

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    Using a Self-Organising Map (SOM), the structure of a data set can be explored when analysing patterns between data that are multivariate, nonlinear and unlabelled in nature. As a SOM alone cannot be used to explore patterns between different data sets, a similarity weighting scheme was previously introduced to associate different SOMs in a network fashion and approximate output patterns for given inputs. This approach uses a global weight association method on the combination of all SOMs specified for a network. However, there has been a difficulty in defining the association when changing the SOM network structure. Furthermore, it has always produced the same output weight distribution for different input data that have the same best matching unit. In an attempt to overcome the issues, we propose a new approach in this paper for locally associating a pair of SOMs as a basic network building block and approximating individually associated weight distribution. The experiments using ecological data demonstrate that the proposed approach effectively associates a pair of input and output SOMs for structural flexibility of the SOM network with better approximation of output weight distributions for individual input data.

    Original languageEnglish
    Title of host publicationNeural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
    EditorsTom Gedeon, Kok Wai Wong, Minho Lee
    PublisherSpringer
    Pages779-788
    Number of pages10
    ISBN (Print)9783030368012
    DOIs
    Publication statusPublished - 2019
    Event26th International Conference on Neural Information Processing, ICONIP 2019 - Sydney, Australia
    Duration: 12 Dec 201915 Dec 2019

    Publication series

    NameCommunications in Computer and Information Science
    Volume1143 CCIS
    ISSN (Print)1865-0929
    ISSN (Electronic)1865-0937

    Conference

    Conference26th International Conference on Neural Information Processing, ICONIP 2019
    Country/TerritoryAustralia
    CitySydney
    Period12/12/1915/12/19

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