A Fast Synchronization Clustering Algorithm

TL;DR

This paper introduces FSynC, a faster clustering algorithm that improves upon SynC by using grid partitioning and Red-Black trees, demonstrating reduced computation time on various datasets.

Contribution

The paper presents FSynC, an enhanced synchronization clustering algorithm that significantly reduces time complexity through innovative data structure integration.

Findings

FSynC outperforms SynC in execution time on artificial and real datasets.

The combination of grid partitioning and Red-Black trees improves clustering efficiency.

Experimental results validate the effectiveness of FSynC.

Abstract

This paper presents a Fast Synchronization Clustering algorithm (FSynC), which is an improved version of SynC algorithm. In order to decrease the time complexity of the original SynC algorithm, we combine grid cell partitioning method and Red-Black tree to construct the near neighbor point set of every point. By simulated experiments of some artificial data sets and several real data sets, we observe that FSynC algorithm can often get less time than SynC algorithm for many kinds of data sets. At last, it gives some research expectations to popularize this algorithm.