Methods and Algorithms of Cluster Analysis in the Mining Industry - Solution of Tasks for Mineral Rocks Recognition

2014

Full text Open access: Hybrid Metadata (CSL-JSON)
DOI 10.5220/0005022901650171
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

It is described the algorithm for automatic segmentation of colour images of ores, using the methods of cluster analysis. There are some examples illustrated using of the algorithm in the solving of mineral rock recognition problems. Results of studies are demonstrated different colour spaces by k-means clustering. It was supposed the technique of pre-computing the values of the centroids. There is formulas translation metrics colour space HSV. The effectiveness of the proposed method lies in the automatic identification of interest objects on the total image, tuning parameters of the algorithm is a number that indicates the amount allocated to the segments. This paper contains short description of cluster analysis algorithm for the mineral rock recognition in the mining industry.