TNStream: Tightest Neighbors for Multi-Density Data Stream Clustering
The recently introduced clustering algorithm, TNStream, tackles issues related to data stream clustering by effectively managing high-dimensional, multi-density data with arbitrary shapes and robust outlier resistance. It employs an innovative approach known as Tightest Neighbors alongside a Skeleton Set theory to dynamically establish the clustering radius based on local similarities. This method captures the progression of multi-density data streams through micro-clusters and utilizes a Tightest Neighbors-based technique to create the final clusters. TNStream operates as a fully online algorithm aimed at enhancing efficiency within high-dimensional streaming data contexts. This research was made available on arXiv under ID 2505.00359.
Key facts
- TNStream is a fully online clustering algorithm.
- It uses the concept of Tightest Neighbors and Skeleton Set theory.
- The algorithm adaptively determines clustering radius based on local similarity.
- It handles arbitrarily shaped, multi-density, high-dimensional data.
- The algorithm maintains strong outlier resistance.
- It summarizes data stream evolution in micro-clusters.
- The paper is available on arXiv with ID 2505.00359.
- The algorithm aims to improve efficiency in high-dimensional streaming data.
Entities
Institutions
- arXiv