Clustering West Nile Virus Spatio-temporal data using ST-DBSCAN

Chimwayi, Kerina Blessmore; Anuradha, J.

doi:10.1016/j.procs.2018.05.037

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…The well-known DBSCAN algorithm (Ester et al, 1996) and its spatio-temporal adaptation ST-DBSCAN (Birant & Kut, 2007) are flexible and efficient ways of clustering spatial and spatio-temporal data (Catlett et al, 2019;Chimwayi & Anuradha, 2018;Gomide et al, 2011). Due to its nature, DBSCAN suffers from a weakness when presented with clusters of varying densities.…”

Section: Discussionmentioning

confidence: 99%

Modelling antimicrobial prescriptions in Scotland: A spatio-temporal clustering approach

Gieschen,

Ansell,

Calabrese

et al. 2019

Preprint

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In 2016 the British government acknowledged the importance of reducing antimicrobial prescriptions in order to avoid the long-term harmful effects of over-prescription. Prescription needs are highly dependent on factors that have a spatio-temporal component, such as the presence of a bacterial outbreak and the population density. In this context, density-based clustering algorithms are flexible tools to analyse data by searching for group structures. The case of Scotland presents an additional challenge due to the diversity of population densities under the area of study. We present here a spatio-temporal clustering approach for highlighting the behaviour of general practitioners (GPs) in Scotland.Particularly, we consider the density-based spatial clustering of applications with noise algorithm (DBSCAN) due to its ability to include both spatial and temporal data, as well as its flexibility to be extended with further variables. We extend this approach into two directions. For the temporal analysis, we use dynamic time warping to measure the dissimilarity between warped and shifted time series. For the spatial component, we introduce a new way of weighting spatial distances with continuous weights derived from a KDE-based process. This makes our approach suitable for cases involving spatial clusters with differing densities, which is a well-known issue for the original DBSCAN. We show an improved performance compared to both the latter and the popular k-means algorithm on simulated, as well as empirical data, presenting evidence for the ability to cluster more elements correctly and deliver actionable insights.

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Section: Discussionmentioning

confidence: 99%

Modelling antimicrobial prescriptions in Scotland: A spatio-temporal clustering approach

Gieschen,

Ansell,

Calabrese

et al. 2019

Preprint

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show abstract

“…The neighborhood threshold η means that the number of points needed within a region to consider it a cluster can be chosen depending on the application context. For example, Chimwayi and Anuradha (2018) try different values for η as well as ε 1 and ε 2 in their analysis of the spatiotemporal spread of the Chicago West Nile Virus, and compare the results according to the number of noise points. In the case of Andraud, Bougeard, Chesnoiu, & Rose (2021) and their analysis of an African swine fever outbreak in Romania, they use the average number of outbreaks in infected communes as their η.…”

Section: Clustering Using Dbscan and St-dbscanmentioning

confidence: 99%

Modeling Antimicrobial Prescriptions in Scotland: A Spatiotemporal Clustering Approach

et al. 2021

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In 2016, the British government acknowledged the importance of reducing antimicrobial prescriptions to avoid the long-term harmful effects of overprescription. Prescription needs are highly dependent on the factors that have a spatiotemporal component, such as bacterial outbreaks and urban densities. In this context, density-based clustering algorithms are flexible tools to analyze data by searching for group structures and therefore identifying peer groups of GPs with similar behavior. The case of Scotland presents an additional challenge due to the diversity of population densities under the area of study. We propose here a spatiotemporal clustering approach for modeling the behavior of antimicrobial prescriptions in Scotland. Particularly, we consider the density-based spatial clustering of applications with noise algorithm (DBSCAN) due to its ability to include both spatial and temporal data. We extend this approach into two directions. For the temporal analysis, we use dynamic time warping to measure the dissimilarity between time series while taking into account effects such as seasonality. For the spatial component, we propose a new way of weighting spatial distances with continuous weights derived from a Kernel density estimation-based process. This makes our approach suitable for cases with different local densities, which presents a well-known challenge for the original DBSCAN. We apply our approach to antibiotic prescription data in Scotland, demonstrating how the findings can be used to compare antimicrobial prescription behavior within a group of similar peers and detect regions of extreme behaviors.

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“…(6) Iterations are repeated until the sample group no longer changes. Square error is usually used as a criterion for convergence of functions [5], [6].…”

Section: Application Of K-means Algorithm For Costumer Grouping Joannmentioning

confidence: 99%

Application of K-Means Algorithm for Costumer Grouping

Nugraha¹

2020

IJCTE

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Sales fluctuations are risks that must be faced by business people, PT. Gunung Hijau Success experienced this in 2016, with GAP being quite high. By giving rewards to loyal customers, it is expected to stabilize sales in the next period. So the company needs customer grouping based on customer loyalty to reward. The application of data mining can be used as an analysis to determine the loyal customer inventory according to the total purchase. In the data mining method, the clustering algorithm is one of the most popular to use where the data belonging to the same cluster will be close to each other and will be far from the data about another cluster. The results obtained in the form of customer information with criteria not loyal, loyal, and very loyal based on sales data in 2016. Also, customer criteria information from the clustering process can be used as a reference to determine the reward for customers.

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Clustering West Nile Virus Spatio-temporal data using ST-DBSCAN

Cited by 11 publications

References 11 publications

Modelling antimicrobial prescriptions in Scotland: A spatio-temporal clustering approach

Modelling antimicrobial prescriptions in Scotland: A spatio-temporal clustering approach

Modeling Antimicrobial Prescriptions in Scotland: A Spatiotemporal Clustering Approach

Application of K-Means Algorithm for Costumer Grouping

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