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

• Published in: EURASIP journal on bioinformatics & systems biology Vol. 2010; no. 1; pp. 1 - 14
• Main Author: Oyana, Tonny J
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 2010; Springer Nature B.V; Springer
• Subjects: 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
• DOI: 10.1155/2010/746021

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.