A new affinity matrix weighted k-nearest neighbors graph to improve spectral clustering accuracy

Researchers have thought about clustering approaches that incorporate traditional clustering methods and deep learning techniques. These approaches normally boost the performance of clustering. Getting knowledge from large data-sets is quite an interesting task. In this case, we use some dimensionality reduction and clustering techniques. Spectral clustering is gaining popularity recently because of its performance. Lately, numerous techniques have been introduced to boost spectral clustering performance. One of the most significant part of these techniques is to construct a similarity graph. We introduced weighted k-nearest neighbors technique for the construction of similarity graph. Using this new metric for the construction of affinity matrix, we achieved good results as we tested it both on real and artificial data-sets.

Automated summary

Researchers have proposed clustering approaches that combine traditional clustering methods with deep learning techniques to improve clustering performance. Spectral clustering has become popular due to its performance, with various techniques introduced to enhance its performance, such as constructing a similarity graph. Introducing the weighted k-nearest neighbors technique for constructing the similarity graph has shown promising results on both real and artificial datasets.