Self-Tuning p-Spectral Clustering Based on Shared Nearest Neighbors

Abstract

Cognitive computing needs to handle large amounts of data and information. Spectral clustering is a powerful data mining tool based on algebraic graph theory. Because of the solid theoretical foundation and good clustering performance, spectral clustering has aroused extensive attention of academia in recent years. Spectral clustering transforms the data clustering problem into the graph partitioning problem. Cheeger cut is an optimized graph partitioning criterion. To minimize the objective function of Cheeger cut, the eigen-decomposition of p-Laplacian matrix is required. However, the clustering results are sensitive to the selection of similarity measurement and the parameter p of p-Laplacian matrix. Therefore, we propose a self-tuning p-spectral clustering algorithm based on shared nearest neighbors (SNN-PSC). This algorithm uses shared nearest neighbors to measure the similarities of data couples and then applies fruit fly optimization algorithm to find the optimal parameters p of p-Laplacian matrix that leads to better data classification. Experiments show that SNN-PSC algorithm can produce more balanced clusters and has strong adaptability and robustness compared to traditional spectral clustering algorithms.