Blended Clustering for Health Data Mining - E-Health Access content directly
Conference Papers Year : 2010

Blended Clustering for Health Data Mining

Abstract

Exploratory data analysis using data mining techniques is becoming more popular for investigating subtle relationships in health data, for which direct data collection trials would not be possible. Health data mining involving clustering for large complex data sets in such cases is often limited by insufficient key indicative variables. When a conventional clustering technique is then applied, the results may be too imprecise, or may be inappropriately clustered according to expectations. This paper suggests an approach which can offer greater range of choice for generating potential clusters of interest, from which a better outcome might in turn be obtained by aggregating the results. An example use case based on health services utilization characterization according to socio-demographic background is discussed and the blended clustering approach being taken for it is described.
Fichier principal
Vignette du fichier
o-13wcc2010final00451.pdf (181.88 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01054865 , version 1 (08-08-2014)

Licence

Attribution

Identifiers

Cite

Arshad Muhammad Mehar, Anthony Maeder, Kenan Matawie, Athula Ginige. Blended Clustering for Health Data Mining. First IMIA/IFIP Joint Symposium on E-Health (E-HEALTH) / Held as Part of World Computer Congress (WCC), Sep 2010, Brisbane, Australia. pp.130-137, ⟨10.1007/978-3-642-15515-4_14⟩. ⟨hal-01054865⟩
93 View
297 Download

Altmetric

Share

Gmail Facebook X LinkedIn More