Forthcoming and Online First Articles

International Journal of Knowledge Engineering and Data Mining

International Journal of Knowledge Engineering and Data Mining (IJKEDM)

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International Journal of Knowledge Engineering and Data Mining (1 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