Sparse Neutrosophic Clustering Algorithm Based on Local Preserving Projection
Clustering algorithm is one of the important research topics in the field of machine learning.Traditional neutrosoph-ic clustering algorithms(such as FC-PFS algorithm)do not consider the local spatial structure,and the calculation of distance is af-fected by redundant features and cannot effectively process high-dimensional data set.A new sparse neutrosophic clustering algo-rithm(LPSNCM)based on local preserved projection and its optimization method are proposed in this paper.On the one hand,an orthogonal projection space with local structure information is generated by the local preserved projection method in the LPSNCM al-gorithm,on the other hand,the feature extraction method can reduce the number of features to obtain more effective features,thus enhancing the capability of FC-PFS algorithm to process high-dimensional data.The LPSNCM algorithm can also be regarded as a unified model of the two independent stages of spectral clustering.Experimental results on some benchmark datasets demonstrate the effectiveness of LPSNCM compared with FC-PFS and some of the latest methods.
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