Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density

Yousheng Gao, Raihah Aminuddin, Raseeda Hamzah, Li Ang, Siti Khatijah Nor Abdul Rahim
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引用次数: 0

Abstract

In the absence of supervisory information in spectral clustering algorithms, it is difficult to construct suitable similarity graphs for data with complex shapes and varying densities. To address this issue, this paper proposes a Semi-supervised Spectral Clustering algorithm based on shared nearest neighbor. The proposed algorithm combines the idea of semi-supervised clustering, adding Shared Nearest Neighbor information to the calculation of the distance matrix, and using pairwise constraint information to find the relationship between two data points, while providing a portion of supervised information. Comparative experiments were conducted on artificial data sets and University of California Irvine machine learning repository datasets. The experimental results show that the proposed algorithm achieves better clustering results compared to traditional K-means and spectral clustering algorithms.
利用共享近邻对不同形状和密度的数据进行半监督光谱聚类
在光谱聚类算法中缺乏监督信息的情况下,很难为形状复杂、密度各异的数据构建合适的相似性图。针对这一问题,本文提出了一种基于共享近邻的半监督光谱聚类算法。所提算法结合了半监督聚类的思想,在计算距离矩阵时加入了共享近邻信息,利用成对约束信息找到两个数据点之间的关系,同时提供了一部分监督信息。在人工数据集和加州大学欧文分校机器学习库数据集上进行了对比实验。实验结果表明,与传统的 K-means 聚类算法和光谱聚类算法相比,所提出的算法取得了更好的聚类效果。
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