Visualization of dengue incidences for vulnerability using K-means

Nirbhay Mathur, Vijanth S. Asirvadam, Sarat C. Dass, Balvinder Singh Gill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Dengue is the world's most rapidly spreading and geographically widespread arthropod-borne disease. Dengue epidemics are observed to be larger, more frequent and associated with more severe disease than they were in the past. To control the incidence of the disease, it is important to be able to identify the hot-spots localized regions of high incidences. This work focuses on identifying hot-spots of dengue using the K-means clustering algorithm. Data is collected from the state of Selangor in Malaysia from 2013 to 2014. Visualization of dengue vulnerability is obtained via Gaussian mixture models fitted using K-means algorithm. Results demonstrate the ability to render visualization for the vulnerability of dengue incidences on the basis of high density and low density cluster using Gaussian mixture and K-means algorithm.

Original languageEnglish
Title of host publicationIEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings
PublisherIEEE
Pages569-573
Number of pages5
ISBN (Electronic)9781479989966
DOIs
Publication statusPublished - 25 Feb 2016
Event4th IEEE International Conference on Signal and Image Processing Applications 2015 - Kuala Lumpur, Malaysia
Duration: 19 Oct 201521 Oct 2015

Conference

Conference4th IEEE International Conference on Signal and Image Processing Applications 2015
Abbreviated titleICSIPA 2015
CountryMalaysia
CityKuala Lumpur
Period19/10/1521/10/15

Keywords

  • clustering
  • Dengue
  • Gaussian Mixture model
  • Geographical Information System (GIS)
  • K-means

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing

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  • Cite this

    Mathur, N., Asirvadam, V. S., Dass, S. C., & Gill, B. S. (2016). Visualization of dengue incidences for vulnerability using K-means. In IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings (pp. 569-573). IEEE. https://doi.org/10.1109/ICSIPA.2015.7412255