In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts of current clustering algorithms. Combined with single-pass and online incremental strategies, respectively, we propose two incremental fuzzy clustering algorithms based on feature reduction. The first uses the Weighted Feature Reduction Fuzzy C-Means (WFRFCM) clustering algorithm to process each chunk in turn and combines the clustering results of the previous chunk into the latter chunk for common calculation. The second uses the WFRFCM algorithm for each chunk to cluster at the same time, and the clustering results of each chunk are combined and calculated again. In order to investigate the clustering performance of these two algorithms, six datasets were selected for comparative experiments. Experimental results showed that these two algorithms could select high-quality features based on feature reduction and process large-scale data by introducing the incremental strategy. The combination of the two phases can not only ensure the clustering efficiency but also keep higher clustering accuracy.

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In traditional density peak clustering, when the density distribution of samples in a dataset is uneven, the density peak points are often concentrated in the region with dense sample distribution, which is easy to affect clustering accuracy. Under the progressive allocation strategy, a density peak clustering algorithm based on relative density is proposed in this paper. This algorithm uses the K-nearest neighbor method to calculate the local density of sample points. In addition, in order to avoid the domino effect during sample allocation, a new similarity calculation method is defined, and a progressive allocation strategy from near to far is used for the allocation of the remaining points. In order to evaluate the effectiveness of this algorithm, comparative experiments with five algorithms were carried out on classical artificial datasets and real datasets. Experimental results show that the proposed algorithm can achieve higher clustering accuracy on datasets with uneven density distribution.

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