TNStream: Applying Tightest Neighbors to Micro-Clusters to Define Multi-Density Clusters in Streaming Data

TL;DR

TNStream is a novel online clustering algorithm for streaming data that adaptively identifies multi-density clusters using Tightest Neighbors and Locality-Sensitive Hashing, improving clustering quality in complex data streams.

Contribution

The paper introduces TNStream, a fully online clustering method based on Tightest Neighbors and Skeleton Set theory, addressing multi-density and high-dimensional data challenges.

Findings

TNStream effectively handles multi-density data streams.

Experimental results show improved clustering quality over existing methods.

LSH enhances efficiency in high-dimensional clustering scenarios.

Abstract

In data stream clustering, systematic theory of stream clustering algorithms remains relatively scarce. Recently, density-based methods have gained attention. However, existing algorithms struggle to simultaneously handle arbitrarily shaped, multi-density, high-dimensional data while maintaining strong outlier resistance. Clustering quality significantly deteriorates when data density varies complexly. This paper proposes a clustering algorithm based on the novel concept of Tightest Neighbors and introduces a data stream clustering theory based on the Skeleton Set. Based on these theories, this paper develops a new method, TNStream, a fully online algorithm. The algorithm adaptively determines the clustering radius based on local similarity, summarizing the evolution of multi-density data streams in micro-clusters. It then applies a Tightest Neighbors-based clustering algorithm to form final clusters. To improve efficiency in high-dimensional cases, Locality-Sensitive Hashing (LSH) is employed to structure micro-clusters, addressing the challenge of storing k-nearest neighbors. TNStream is evaluated on various synthetic and real-world datasets using different clustering metrics. Experimental results demonstrate its effectiveness in improving clustering quality for multi-density data and validate the proposed data stream clustering theory.