Remote sensing analysis of oilfield IoT based on density clustering

In this paper, a density-based neighborhood search clustering method is proposed. A decision graph is obtained through density calculation, this method only needs to select density centers, and clustering results can be achieved automatically without irregular and frequent parameter adjustment. The algorithm is suitable for industrial big data clustering. It not only explains the clustering results vividly, but also increases or decreases the amount of clustering rapidly according to the need. Experimental results show that this method performs well on artificial data sets in contrast to the traditional methods and is successfully applied to a real oilfield engineering case.