Clustering Big Spatiotemporal-Interval Data

Publication Year: 2016 Publication Type : JournalArticle


We propose a model for clustering data with spatiotemporal intervals, which is a type of spatiotemporal data associated with a start- and an end-point. This model can be used to effectively evaluate clusters of spatiotemporal interval data, which signifies an event at a particular location that stretches over a period of time. Our work aims to deal with evaluating the results of clustering in multiple Euclidean spaces. This is different from traditional clustering that measure results in single Euclidean space. A new energy function is proposed that measures similarity and balance between clusters in spatial, temporal, and data dimensions. A large collection of parking data from a real CBD area is used as a case study. The proposed model is applied to existing traditional algorithms to solve spatiotemporal interval data clustering problem. Using the proposed energy function, the results of traditional clustering algorithms are compared and analysed.


    author = {Wei Shao and Flora D. Salim and Andy Song and Athman Bouguettaya},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    doi = {10.1109/TBDATA.2016.2599923},
    journal = {IEEE Trans. Big Data},
    number = {3},
    pages = {190--203},
    timestamp = {Wed, 24 Oct 2018 01:00:00 +0200},
    title = {Clustering Big Spatiotemporal-Interval Data},
    url = {},
    volume = {2},
    year = {2016}


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