Procedia Computer Science · 2018 · 12 citations · 9 references
Veterinary MedicineInfectious DiseasesPublic Health InformaticsData MiningSpatial AnalysisSpatial Data AnalysisCluster DetectionDisease SpreadSpatial Cluster AnalysisSpatial ClusteringSpatial Distribution Of DiseaseSurveillance Data AnalysisDisease Cluster AnalysisSpatial Clustering AnalysisSpatial Clustering Of DiseaseSpatial-temporal Cluster AnalysisSpatial-temporal ClusteringSpatio-temporal Data MiningSpatiotemporal Data MiningTemporal Cluster Detection
Spatio-temporal data mining has been the talk of the day due to high availability of spatio-temporal data from varied sources in diverse fields. Through many tracking devices, huge amounts of spatio-temporal data are being generated. In epidemiology, diseases, patterns and trends attached can be explored taking advantage of methods such as spatio-temporal clustering to discover new knowledge. In this paper Spatio-Temporal Density Based Spatial Clustering of Applications with Noise (ST-DBSCAN) is implemented and analysed on a public health dataset. Upon the implementation, results are analysed, loopholes spotted and a fuzzy version of ST-DBSCAN is proposed. The method is successfully applied to find spatio-temporal clusters in Chicago West Nile Virus (WNV) surveillance data for the period 2007 to 2017.The drawbacks in the original ST-DBSCAN are identified and solutions are proposed. ST-DBSCAN is an extension of the original Density Based Spatial Clustering of Applications with Noise (DBSCAN).
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ST-DBSCAN: An algorithm for clustering spatial–temporal data
Derya Birant, Alp Kut · Data & Knowledge Engineering · 2006 · 1.4K citations
Density-Based Clustering Validation
Davoud Moulavi, Pablo Andretta Jaskowiak, Ricardo J. G. B. Campello et al. · 2014 · 262 citations