A complete gradient clustering algorithm for features analysis of X-ray images
Autor
Charytanowicz, Małgorzata
Niewczas, Jerzy
Kulczycki, Piotr
Kowalski, Piotr Andrzej
Łukasik, Szymon
Żak, Sławomir
Data wydania
2010
Miejsce wydania
Berlin ; Heidelberg
Wydawca
Springer
Opublikowane w
Information Technologies in Biomedicine / red. Ewa Pietka, Jacek Kawa
Strony
15-24
Język
angielski
ISBN
978-3-642-13104-2
DOI
10.1007/978-3-642-13105-9_2
Abstrakt
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.
