Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article A New-Fangled FES-k -Means Clustering Algorithm for Disease Discovery and Visual Analytics | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2010 Article ID 746021 14 pages doi 2010 746021 Research Article A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics Tonny J. Oyana GIS Research Laboratory for Geographic Medicine Advanced Geospatial Analysis Laboratory Department of Geography Environmental Resources Southern Illinois University 1000 Faner Drive MC4514 Carbondale IL 62901-4514 USA Correspondence should be addressed to Tonny J. Oyana tjoyana@ Received 22 November 2009 Revised 27 April 2010 Accepted 7 May 2010 Academic Editor Haiyan Hu Copyright 2010 Tonny J. Oyana. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 .
