A New K-NN-Centroid-Inspired Density-Based Clustering Algorithm

Density-based clustering algorithms are well known for identifying clusters possessing very different local densities and existing in different regions of data space. However, the parameters required by most popular density-based clustering algorithms, such as DBSCAN, are hard to determine but have significant impacts on the clustering results. In this paper, we present a new density-based clustering algorithm in which the selection of appropriate parameters is less difficult but more meaningful. Experiments performed on several datasets show the effectiveness of our approach.