Classification of infectious diseases via hybrid k-means clustering technique
Identifying groups of objects that are similar to each other but different from individuals in other groups can be intellectually satisfying, profitable, or sometimes both. Kmeans clustering is one of the well known partitioning algorithms. But basic K-means method is insufficient to extract meaning...
Saved in:
Main Authors: | , |
---|---|
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2015
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/61822/1/IsmailMohamad2015_ClassificationofInfectiousDiseasesViaHybridK-MeansClusteringTechnique.pdf http://eprints.utm.my/id/eprint/61822/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.61822 |
---|---|
record_format |
eprints |
spelling |
my.utm.618222017-08-10T00:49:04Z http://eprints.utm.my/id/eprint/61822/ Classification of infectious diseases via hybrid k-means clustering technique Usman, Dauda Mohamad, Ismail TA Engineering (General). Civil engineering (General) Identifying groups of objects that are similar to each other but different from individuals in other groups can be intellectually satisfying, profitable, or sometimes both. Kmeans clustering is one of the well known partitioning algorithms. But basic K-means method is insufficient to extract meaningful information and its output is very conscious to initial positions of cluster centers. In this paper, data of infectious diseases were analyzed with the hybrid K-means clustering technique. This method is developed to preprocess the dataset that will be used in the K-means clustering problems. Specifically, it performs K-means clustering on preprocessed dataset instead of raw dataset to remove the impact of irrelevant features and selection of good initial centers. The experimental results revealed that all the three water related diseases are grouped together in one cluster for both KGHK and FMCK data sets. They also show the high prevalence compared to airborne particle related diseases in the other group. The study concludes that K-means clustering method provides a suitable tool for assessing the level of infectious diseases. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/61822/1/IsmailMohamad2015_ClassificationofInfectiousDiseasesViaHybridK-MeansClusteringTechnique.pdf Usman, Dauda and Mohamad, Ismail (2015) Classification of infectious diseases via hybrid k-means clustering technique. In: 3rd International Science Postgraduate Conference (ISPC 2015), 24-26 Feb,2015, Johor Bahru, Johor. |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
TA Engineering (General). Civil engineering (General) |
spellingShingle |
TA Engineering (General). Civil engineering (General) Usman, Dauda Mohamad, Ismail Classification of infectious diseases via hybrid k-means clustering technique |
description |
Identifying groups of objects that are similar to each other but different from individuals in other groups can be intellectually satisfying, profitable, or sometimes both. Kmeans clustering is one of the well known partitioning algorithms. But basic K-means method is insufficient to extract meaningful information and its output is very conscious to initial positions of cluster centers. In this paper, data of infectious diseases were analyzed with the hybrid K-means clustering technique. This method is developed to preprocess the dataset that will be used in the K-means clustering problems. Specifically, it performs K-means clustering on preprocessed dataset instead of raw dataset to remove the impact of irrelevant features and selection of good initial centers. The experimental results revealed that all the three water related diseases are grouped together in one cluster for both KGHK and FMCK data sets. They also show the high prevalence compared to airborne particle related diseases in the other group. The study concludes that K-means clustering method provides a suitable tool for assessing the level of infectious diseases. |
format |
Conference or Workshop Item |
author |
Usman, Dauda Mohamad, Ismail |
author_facet |
Usman, Dauda Mohamad, Ismail |
author_sort |
Usman, Dauda |
title |
Classification of infectious diseases via hybrid k-means clustering technique |
title_short |
Classification of infectious diseases via hybrid k-means clustering technique |
title_full |
Classification of infectious diseases via hybrid k-means clustering technique |
title_fullStr |
Classification of infectious diseases via hybrid k-means clustering technique |
title_full_unstemmed |
Classification of infectious diseases via hybrid k-means clustering technique |
title_sort |
classification of infectious diseases via hybrid k-means clustering technique |
publishDate |
2015 |
url |
http://eprints.utm.my/id/eprint/61822/1/IsmailMohamad2015_ClassificationofInfectiousDiseasesViaHybridK-MeansClusteringTechnique.pdf http://eprints.utm.my/id/eprint/61822/ |
_version_ |
1643655272202240000 |
score |
13.160551 |