Improved Spectral Clustering Based on Density Combining DNA Genetic Algorithm

Abstract

Spectral clustering has become very popular in recent years, due to the simplicity of its implementation as well as the performance of the method, in comparison with other popular ones. But many studies show that clustering results are sensitive to the selection of the similarity graph and its parameters, e.g. [Formula: see text] and [Formula: see text]. To address this issue, inspired by density sensitive similarity measure, we propose an improved spectral graph clustering method that utilizes the similarity measure based on data density combined with DNA genetic algorithms (ISC-DNA-GA), making it increase the distance of the pairs of data in the high density areas, which are located in different spaces. The method can reduce the similarity degree among the pairs of data in the same density region to find the spatial distribution characteristics of the complex data. After computing the Laplacian matrix, we apply DNA-GAs to obtain the clustering centroids and assign all of the points to the centroids, so as to achieve better clustering results. Experiments have been conducted on the artificial and real-world datasets with various multi-dimensions, using evaluation methods based on external clustering criteria. The results show that the proposed method improves the spectral clustering quality, and it is superior to those competing approaches.

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