Analyzing the fast search and find of density peaks clustering (DPC) algorithm, we find that the cluster centers cannot be determined automatically and that the selected cluster centers may fall into a local optimum and the random selection of the parameter cut-off distance dc value. To overcome these problems, a novel clustering algorithm based on DPC & PSO (PDPC) is proposed. Particle swarm optimization (PSO) is introduced because of its simple concept and strong global search ability, which can find the optimal solution in relatively few iterations. First, to solve the effect of the selection of the parameter dc on the calculation density and the clustering results, this paper proposes a method to calculate that parameter. Second, a new fitness criterion function is proposed that iteratively searches K global optimal solutions through the PSO algorithm, that is, the initial cluster centers. Third, each sample is assigned to K initial center points according to the minimum distance principle. Finally, we update the cluster centers and redistribute the remaining objects to the clusters closest to the cluster centers. Furthermore, the effectiveness of the proposed algorithm is verified on nine typical benchmark data sets. The experimental results show that the PDPC can effectively solve the problem of cluster center selection in the DPC algorithm, avoiding the subjectivity of the manual selection process and overcoming the influence of the parameter dc. Compared with the other six algorithms, the PDPC algorithm has a stronger global search ability, higher stability and a better clustering effect.

The extensive growth of data quantity has posed many challenges to data analysis and retrieval. Noise and redundancy are typical representatives of the above-mentioned challenges, which may reduce the reliability of analysis and retrieval results and increase storage and computing overhead. To solve the above problems, a two-stage data pre-processing framework for noise identification and data reduction, called ARIS, is proposed in this article. The first stage identifies and removes noises by the following steps: First, the influence space (IS) is introduced to elaborate data distribution. Second, a ranking factor (RF) is defined to describe the possibility that the points are regarded as noises, then, the definition of noise is given based on RF. Third, a clean dataset (CD) is obtained by removing noise from the original dataset. The second stage learns representative data and realizes data reduction. In this process, CD is divided into multiple small regions by IS. Then the reduced dataset is formed by collecting the representations of each region. The performance of ARIS is verified by experiments on artificial and real datasets. Experimental results show that ARIS effectively weakens the impact of noise and reduces the amount of data and significantly improves the accuracy of data analysis within a reasonable time cost range.