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; p. 746021
• Main Author: Oyana, Tonny J
• Format: Journal Article
• Language: English
• Published: New York Springer Nature B.V 08.06.2010; BioMed Central Ltd
• Subjects: Algorithms; Blood levels; Data mining; Field study; Gene expression; Human subjects; Mean square errors; Methods; Quality
• ISSN: 1687-4145; 1687-4153; 1687-4153
• DOI: 10.1186/1687-4153-2010-746021

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.