A two-stage density clustering algorithm

Abstract

Clustering by fast search and find of density peaks (CFDP) is a popular density-based algorithm. However, it is criticized because it is inefficient and applicable only to some types of data, and requires the manual setting of the key parameter. In this paper, we propose the two-stage density clustering algorithm, which takes advantage of granular computing to address the aforementioned issues. The new algorithm is highly efficient, adaptive to various types of data, and requires minimal parameter setting. The first stage uses the two-round-means algorithm to obtain $$\sqrt{n}$$ small blocks, where n is the number of instances. This stage decreases the data size directly from n to $$\sqrt{n}$$ . The second stage constructs the master tree and obtains the final blocks. This stage borrows the structure of CFDP, while the cutoff distance parameter is not required. The time complexity of the algorithm is $$O(mn^\frac{3}{2})$$ , which is lower than $$O (mn^2)$$ for CFDP. We report the results of some experiments performed on 21 datasets from various domains to compare a new clustering algorithm with some state-of-the-art clustering algorithms. The results demonstrated that the new algorithm is adaptive to different types of datasets. It is two or more orders of magnitude faster than CFDP.