A Fast Synchronization Clustering Algorithm
This is an incremental improvement for researchers and practitioners in data clustering, offering a more efficient version of an existing algorithm.
The paper tackles the high time complexity of the SynC clustering algorithm by introducing FSynC, which uses grid cell partitioning and Red-Black trees to reduce computational time, achieving faster performance on various artificial and real datasets.
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.