An improved density-based adaptive p-spectral clustering algorithm

• Published in: International journal of machine learning and cybernetics Vol. 12; no. 6; pp. 1571 - 1582
• Main Authors: Wang, Yanru, Ding, Shifei, Wang, Lijuan, Ding, Ling
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Cluster analysis; Clustering; Complex Systems; Computational Intelligence; Control; Data points; Datasets; Density distribution; Engineering; Euclidean geometry; Fuzzy sets; Kernel functions; Mechatronics; Methods; Neighborhoods; Original Article; Pattern Recognition; Robotics; Similarity; Similarity measures; Systems Biology; Spectral clustering; Similarity matrix; laplacian matrix; Density canopy
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-020-01236-x

As a generalization algorithm of spectral clustering, p -spectral clustering has gradually attracted extensive attention of researchers. Gaussian kernel function is generally used in traditional p -spectral clustering to construct the similarity matrix of data. However, the Gaussian kernel function based on Euclidean distance is not effective when the data-set is complex with multiple density peaks or the density distribution is uniform. In order to solve this problem, an improved Density-based adaptive p -spectral clustering algorithm (DAPSC) is proposed, the prior information is considering to adjust the similarity between sample points and strengthen the local correlation between data points. In addition, by combining the density canopy method to update the initial clustering center and the number of clusters, the algorithm sensitivity of the original p -spectral clustering caused by the two is weakened. By experiments on four artificial data-sets and 8F UCI data-sets, we show that the proposed DAPSC has strong adaptability and more accurate compared with the four baseline methods.