Abstract: The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm based on density clustering can automatically perform classification tasks according to data features, and is often used for clustering analysis of complex data sets with noise. However, it has the defects of difficult parameter determination and high degree of human participation, which limits the application of automatic and high-precision mining. To overcome these defects, an adaptive DBSCAN algorithm based on the k-dist graph slope (KSSA-DBSCAN) is proposed. The algorithm can automatically select the appropriate k-dist graph inflection point as the optimal neighborhood based on the slope of the k-dist graph, and automatically determine the optimal density threshold during the clustering iteration process according to the change in the number of clusters, which overcomes the defects of difficult parameter determination and high degree of human participation. KSSA-DBSCAN is compared with DBSCAN and KANN-DBSCAN on six data sets, and the experimental results show that the accuracy of the algorithm is better than that of other algorithms on the four data sets, and the accuracy is increased by up to 25% compared with DBSCAN. When it is applied to the pattern mining of blended MOOC videos viewing behavior data, the results show that the algorithm can effectively and automatically mine the video viewing patterns, further verifying the effectiveness of the algorithm.

Key words: density-based clustering, self-adaptive, k-dist graph, blended MOOC, video viewing pattern