一种基于模板缩减的新型粒子群遗传聚类算法

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Title:

A novel PSO-GGA for clustering based on pattern reduction

作者:

贾旋, 周治平

江南大学 物联网工程学院, 江苏 无锡 214122

Author(s):

JIA Xuan, ZHOU Zhiping

School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China

关键词:

模板缩减; 粒子群; 广义遗传算法; 聚类

Keywords:

pattern reduction; PSO; generalized genetic algorithm; clustering

分类号:

TP18

DOI:

10.11992/tis.201507026

摘要:

针对群聚类算法的速度问题，提出一种基于模板缩减法加速的新型粒子群广义遗传（PSO-GGA）聚类算法。为了充分地同模板缩减法相结合，该算法采用一种广义遗传算法与粒子群算法串行使用，既能增加种群多样性，又能对模板缩减操作中需要保护的模板进行储存。同时，对每个周期替换粒子数量采用一种递增策略来充分吸取粒子群快速寻优和遗传算法搜索空间大的特性。实验表明：对8个数据集进行测试，该算法能够在基本不降低聚类品质的基础上，显著地缩短聚类时间。

Abstract:

To address the flaws in clustering speed, this paper proposes a novel PSO-GGA clustering algorithm based on pattern reduction. To fully combine the pattern reduction method, the algorithm uses a generalized genetic algorithm in serial to improve the particle swarm optimization algorithm. This can increase the diversity of samples and protect patterns that need to be saved for compression. At the same time, to determine the number of particles needed to replace the poor particles an incremental strategy is employed. This fully embodies the PSO’s ability for rapid search optimization and the genetic algorithm’s advantage of a large search space. The experimental results show that the clustering time only required 20 percent compared to the original algorithm without showing any obvious decline in accuracy.