Title :
Unsupervised clustering for Electrofused Magnesium Oxide sorting
Author :
Pun, D. ; Ali, S.
Author_Institution :
Sch. of Comput. Sci., CQUniversity Australia, QLD, Australia
Abstract :
This research is concentrated on using unsupervised learning technique and digital image processing to cluster mineral materials, Electrofused Magnesia Oxide specifically, for industry automation. We proposed a technique to construct an image database by generating data from images using a digital image process. This is based on a simple histogram mode and intensity deviation. A group of two popular clustering algorithms has been tested to develop an automatic system for industry. We have concluded that the best suited algorithm for this application in the mineral industry from this group of two algorithms is the k-means algorithm.
Keywords :
image processing; learning (artificial intelligence); magnesium compounds; minerals; mining industry; pattern clustering; production engineering computing; clustering algorithm; digital image processing; electrofused magnesium oxide sorting; image database; industry automation; intensity deviation; k-means algorithm; mineral materials; simple histogram mode; unsupervised clustering; unsupervised learning; Automation; Clustering algorithms; Digital images; Image databases; Magnesium compounds; Magnesium oxide; Minerals; Mining industry; Sorting; Unsupervised learning; Electrofused Magnesium Oxide; Unsupervised Clustering; k-means;
Conference_Titel :
Industrial Engineering and Engineering Management, 2009. IEEM 2009. IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-4869-2
Electronic_ISBN :
978-1-4244-4870-8
DOI :
10.1109/IEEM.2009.5373234
