Hierarchical clustering is able to provide partitions of different granularity levels. However,
most existing hierarchical clustering techniques perform clustering in the original feature …

Deep clustering incorporates embedding into clustering to find a lower dimensional space
appropriate for clustering. In this paper, we propose a novel deep clustering framework with …

Deep clustering has powerful capabilities of dimensionality reduction and non-linear feature
extraction, superior to conventional shallow clustering algorithms. Deep learning and …

Deep clustering incorporates embedding into clustering to find a lower-dimensional space
appropriate for clustering. In this paper, we propose a novel deep clustering framework with …

Deep clustering aims to promote clustering tasks by combining deep learning and clustering
together to learn the clustering-oriented representation, and many approaches have shown …

Deep clustering attempts to capture the feature representation that benefits the clustering
issue. Although the existing deep clustering methods have achieved encouraging …

Clustering ensemble integrates multiple base clustering results to improve the stability and
robustness of the single clustering method. It consists of two principal steps: a generation …

Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …

Clustering is an important topic in machine learning and data mining. Recently, deep
clustering, which learns feature representations for clustering tasks using deep neural …

Deep clustering outperforms conventional clustering by mutually promoting representation
learning and cluster assignment. However, most existing deep clustering methods suffer …