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Application of clustering algorithm based on lateral inhibition in university course

The clustering algorithm based on lateral inhibition not only inherits the excellent robustness of self-organizing feature map (SOM) neural network, but also the superior performance as spectral clustering when only similarity information is provided. In this algorithm, first of all, the samples should be organized into a graph according to their similarity information. Then their eigenvector space is constructed. Finally, data in the eigenvector space are clustered by SOM neural network based on lateral inhibition. Using this algorithm, we made clustering analysis on 53 compulsory courses in college of computer science and technology, Jilin University. Compared with the traditional clustering methods, the experimental result is more reasonable and precise.