Adaptive density peak clustering algorithm combined with sparse search

Abstract

With the advantages of few parameters and the ability to deal with clusters of arbitrary shape, the density peak clustering algorithm has attracted wide attention since it came out. However, the algorithm has problems such as high time complexity, poor clustering effect on complex data sets, and the need to manually select cluster centers. Aiming at the above shortcomings, an improved density peak clustering algorithm is proposed. Combined with the sparse search algorithm, the calculation of the similarity between each point and its nearest neighbor is simplified, and the problem of the high time complexity of the algorithm is overcome. A new local density definition method is adopted to make the density of data points better reflect the spatial structure of data distribution and to improve the clustering accuracy of the algorithm. Finally, a strategy for automatically selecting cluster centers is proposed to improve the adaptability of the algorithm. The algorithm is used to compare with the other improved algorithm on artificial data sets and real data sets. The experimental results show that the proposed algorithm can show a better clustering effect and can quickly and accurately identify various complex clusters.