A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics. (8th June 2010)

• Record Type: Journal Article
• Authors: Oyana Oyana, Tonny J. Tonny J.
• Other Names: Hu Hu Haiyan Haiyan Academic Editor.
• Is Part Of: EURASIP journal on bioinformatics and systems biology. Volume 2010(2010)
• Publication Date: 2010-06-08
• Subjects: Bioinformatics -- Periodicals; Systems biology -- Periodicals; Systems Biology; Signal Processing, Computer-Assisted; Bio-informatique; Biologie systémique; Bioinformatics; Systems biology; Systems Biology; Bioinformatics; Electronic journals; Periodical; Fulltext; Internet Resources; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2010/746021
• Languages: English
• ISSNs: 1687-4145

Abstract

Abstract : 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.