A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics

The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k -means clustering technique—the Fast, Efficient, and Scalable k...

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Vydáno v:EURASIP journal on bioinformatics & systems biology Ročník 2010; číslo 1; s. 1 - 14
Hlavní autor: Oyana, Tonny J
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 2010
Springer Nature B.V
Springer
Témata:
Algorithms
Bioinformatics
Biology
Biomedical Engineering and Bioengineering
Blood
Blood levels
Cluster analysis
Computational Biology/Bioinformatics
Data mining
Elevated
Engineering
Field study
Gene expression
Human subjects
Mathematical analysis
Mean square errors
Methods
Quality
Research Article
Signal,Image and Speech Processing
Systems Biology
Visual
Cluster Center
Housing Unit
Synthetic Dataset
Neighbor Query
Mean Square Error
ISSN:1687-4145, 1687-4153, 1687-4153
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Shrnutí:The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k -means clustering technique—the Fast, Efficient, and Scalable k -means algorithm ( FES-k -means). The FES-k -means algorithm uses a hybrid approach that comprises the k-d tree data structure that enhances the nearest neighbor query, the original k -means algorithm, and an adaptation rate proposed by Mashor. This algorithm was tested using two real datasets and one synthetic dataset. It was employed twice on all three datasets: once on data trained by the innovative MIL-SOM method and then on the actual untrained data in order to evaluate its competence. This two-step approach of data training prior to clustering provides a solid foundation for knowledge discovery and data mining, otherwise unclaimed by clustering methods alone. The benefits of this method are that it produces clusters similar to the original k -means method at a much faster rate as shown by runtime comparison data; and it provides efficient analysis of large geospatial data with implications for disease mechanism discovery. From a disease mechanism discovery perspective, it is hypothesized that the linear-like pattern of elevated blood lead levels discovered in the city of Chicago may be spatially linked to the city's water service lines.
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ISSN:1687-4145
1687-4153
1687-4153
DOI:10.1155/2010/746021