Complete gradient clustering algorithm for features analysis of X-ray images

作     者：Charytanowicz, Malgorzata Niewczas, Jerzy Kulczycki, Piotr Kowalski, Piotr A. Lukasik, Szymon Zak, Slawomir 

作者机构：Institute of Mathematics and Computer Science John Paul II Catholic University of Lublin Konstantynów 1 H PL 20-708 Lublin Poland Department of Automatic Control and Information Technology Cracow University of Technology Warszawska 24 Cracow PL 31-155 Poland Systems Research Institute Polish Academy of Sciences Newelska 6 Warsaw PL 01-447 Poland 

出 版 物：《Advances in Intelligent and Soft Computing》 (Adv. Intell. Soft Comput.)

年 卷 期：2010年第69卷

页      面：15-24页

核心收录：

主　　题：Clustering algorithms 

摘      要：Methods based on kernel density estimation have been successfully applied for various data mining tasks. Their natural interpretation together with suitable properties make them an attractive tool among others in clustering problems. In this paper, the Complete Gradient Clustering Algorithm has been used to investigate a real data set of grains. The wheat varieties, Kama, Rosa and Canadian, characterized by measurements of main grain geometric features obtained by X-ray technique, have been analyzed. The proposed algorithm is expected to be an effective tool for recognizing wheat varieties. A comparison between the clustering results obtained from this method and the classical k-means clustering algorithm shows positive practical features of the Complete Gradient Clustering Algorithm. © 2010 Springer-Verlag Berlin Heidelberg.