Blended Clustering for Health Data Mining

Mehar, Arshad Muhammad; Maeder, Anthony; Matawie, Kenan M; Ginige, Athula

doi:10.1007/978-3-642-15515-4_14

Cited by 2 publications

(2 citation statements)

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“…Our new approach compares cluster properties at adjacent consecutive k numbers (or a range over several values regarded as a reasonable number) as described in [18]. The underlying idea behind it is an analysis of the "movement" of objects between clusters, considered either forward from k to k+1 or backwards from k+1 to k groups.…”

Section: Proposed Methodsmentioning

confidence: 99%

Determining an optimal value of K in K-means clustering

Mehar¹,

Matawie²,

Maeder³

2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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Partitioning data into a finite number of k homogenous and separate clusters (groups) without use of prior knowledge is carried out by some unsupervised partitioning algorithm like the k-means clustering algorithm. To evaluate these resultant clusters for finding optimal number of clusters, properties such as cluster density, size, shape and separability are typically examined by some cluster validation methods. Mainly the aim of clustering analysis is to find the overall compactness of the clustering solution, for example variance within cluster should be a minimum and separation between the clusters should be a maximum. In this study, for k-means clustering we have developed a new method to find an optimal value of k number of clusters, using the features and variables inherited from datasets. The new proposed method is based on comparison of movement of objects forward/back from k to k+1 and k+1 to k set of clusters to find the joint probability, which is different from the other methods and indexes that are based on the distance. The performance of this method is also compared with some existing methods through two simulated datasets.

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Section: Proposed Methodsmentioning

confidence: 99%

Determining an optimal value of K in K-means clustering

Mehar¹,

Matawie²,

Maeder³

2013

2013 IEEE International Conference on Bioinformatics and Biomedicine

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show abstract

“…In their study [10] considered blended clustering in healthcare data mining. The research observed that in terms of hospital utilization in Australia, length of stay, legal status, age as well as economic situation has an effect in usage of health services.…”

Section: Literature Reviewmentioning

confidence: 99%