A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images

Methods based on kernel density estimation have been successfully applied for various data mining techniques. Their natural interpretation together with consistency properties make them an attractive tool in clustering problems. In this paper, the complete gradient clustering algorithm, based on the density of the data, is presented. The proposed method has been applied to a real data set of grains and compared with K-means clustering algorithm. The wheat varieties, Kama, Rosa and Canadian, characterized by measurements of main grain geometric features obtained by X-ray technique, have been analyzed. Results indicate that the proposed method is expected to be an effective method for recognizing wheat varieties. Moreover, it outperforms the K-means analysis if the nature of the grouping structures among the data is unknown before processing.