SRG: a clustering algorithm based on scale division and region growing

Clustering is an important technique for data mining and machine learning. In this paper, a novel clustering algorithm, scale division and region growing (SRG), is proposed, which is based on scale division and region growing. This method uses the division-and-merge strategy, which consists of two primary phases, namely the division phase and the merging phase. In the division phases, the data is mapped to a two-dimensional space, and the clustering problem is transformed to an image processing problem. Then the image is divided into different scales to adaptively find the appropriate scale. After finding the appropriate scale, the region growing is carried out under this scale and the multiple sub-clusters are created. In the merging phases, the sub-clusters are evaluated and merged to form the final clusters in the dataset. The proposed algorithm is tested on 15 benchmark datasets including both the synthetic datasets and the real datasets, and it is found that SRG can obtain better clustering results and has higher accuracy than other typical clustering algorithms. Moreover, the experimental results on noisy datasets show that the proposed algorithm has good robustness.