A fast divisive clustering algorithm using an improved discrete particle swarm optimizer

As an important technique for data analysis, clustering has been employed in many applications such as image segmentation, document clustering and vector quantization. Divisive clustering, which is a branch of hierarchical clustering, has been studied and used extensively due to its computational efficiency. Generally, which cluster should be split and how to split the selected cluster are two major principles that should be taken into account when a divisive clustering algorithm is used. However, one disadvantage of the divisive clustering is its degraded performance compared to the partitional clustering, thus making it hard to achieve a good trade-off between computational time and clustering performance. To tackle this problem, we propose a novel divisive clustering algorithm by integrating an improved discrete particle swarm optimizer into the divisive framework. Experiments on several synthetic data sets, real-world data sets and two real-world applications (document clustering ...

• Type: Journal
• Year: 2010
• Where: PRL
• Authors: Liang Feng, Ming-Hui Qiu, Yu-Xuan Wang, Qiao-Liang Xiang, Yin-Fei Yang, Kai Liu
• Keywords: Applied Computing, Clustering Algorithms, Divisive Clustering, Divisive Clustering Algorithm, PRL 2010