Forthcoming and Online First Articles

International Journal of Knowledge Engineering and Data Mining

International Journal of Knowledge Engineering and Data Mining (IJKEDM)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Knowledge Engineering and Data Mining (One paper in press)

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  • Distance measures and graph algorithms for clustering time series with applications to gait data   Order a copy of this article
    by Bonan Yang, Gunes Ercal, Sinan Onal 
    Abstract: Time series pattern extraction is important to many domains, especially determining gait abnormalities. The completely unsupervised form of pattern extraction is clustering. The usage of graph-theoretic clustering algorithms for time-series clustering requires distance-based measures to be defined between pairs of data points. Here we evaluate the efficacy of different clustering methods, especially graph-theoretic clustering methodologies, in conjunction with different distance-based measures for unsupervised learning of time-series data. To the best of our knowledge, we are the first to utilise graph-theoretic clustering methods to analyse time-series gait data. We demonstrate the expressiveness of graph-theoretic analysis visually in this context. Our results also support the effectiveness of NBR-clust in the context of time-series data clustering, with more nuanced observations given in the paper.
    Keywords: time-series; clustering; graph theory; gait analysis.
    DOI: 10.1504/IJKEDM.2024.10062710
     

 

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