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19-11-2020 | Original Article

A robust spectral clustering algorithm based on grid-partition and decision-graph

Journal:
International Journal of Machine Learning and Cybernetics
Authors:
Lijuan Wang, Shifei Ding, Yanru Wang, Ling Ding
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Abstract

Spectral clustering (SC) transforms the dataset into a graph structure, and then finds the optimal subgraph by the way of graph-partition to complete the clustering. However, SC algorithm constructs the similarity matrix and feature decomposition for overall datasets, which needs high consumption. Secondly, k-means is taken at the clustering stage and it selects the initial cluster centers randomly, which leads to the unstable performance. Thirdly, SC needs prior knowledge to determine the number of clusters. To deal with these issues, we propose a robust spectral clustering algorithm based on grid-partition and decision-graph (PRSC) to reduce the amount of calculation and improve the clustering efficiency. In addition, a decision-graph method is added to identify the cluster centers quickly to improve the algorithm stability without any prior knowledge. A numerical experiments validate that PRSC algorithm can effectively improve the efficiency of SC. It can quickly obtain the stable performance without any prior knowledge.

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