LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Fast and Effective Active Clustering Ensemble Based on Density Peak

Photo by clevertrevor from unsplash

Semisupervised clustering methods improve performance by randomly selecting pairwise constraints, which may lead to redundancy and instability. In this context, active clustering is proposed to maximize the efficacy of annotations… Click to show full abstract

Semisupervised clustering methods improve performance by randomly selecting pairwise constraints, which may lead to redundancy and instability. In this context, active clustering is proposed to maximize the efficacy of annotations by effectively using pairwise constraints. However, existing methods lack an overall consideration of the querying criteria and repeatedly run semisupervised clustering to update labels. In this work, we first propose an active density peak (ADP) clustering algorithm that considers both representativeness and informativeness. Representative instances are selected to capture data patterns, while informative instances are queried to reduce the uncertainty of clustering results. Meanwhile, we design a fast-update-strategy to update labels efficiently. In addition, we propose an active clustering ensemble framework that combines local and global uncertainties to query the most ambiguous instances for better separation between the clusters. A weighted voting consensus method is introduced for better integration of clustering results. We conducted experiments by comparing our methods with state-of-the-art methods on real-world data sets. Experimental results demonstrate the effectiveness of our methods.

Keywords: density peak; clustering ensemble; fast effective; active clustering

Journal Title: IEEE Transactions on Neural Networks and Learning Systems
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.