| 摘要: |
| 提出了一种新的粒子群密度聚类算法和对粒子群的初始化方法。该算法具有传统粒子群算法寻找最优解的特点,同时从密度的角度考虑了数据总体的分布,增强了寻找局部最优解的能力,并通过对粒子群的初始化加快了粒子群的收敛速度,得到了更好的聚类效果。对仿真数据和IRIS真实数据的实验结果证明,该算法聚类效果优于传统粒子群聚类算法和K均值算法。 |
| 关键词: 聚类分析,粒子群优化算法,初始化,K均值算法,数据分布 |
| DOI： |
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| 基金项目:国防科技预研项目 |
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| A Novel Particle Swarm Optimization Clustering Algorithm Based on Density |
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| Abstract: |
| This paper proposes a novel particle swarm optimization (PSO)clustering algorithm based on density and PSO initialization method.The algorithm not only possesses the characteristic of globe searching capability but also considers distribution of all data from density angle.The algorithm increases the convergence speed and improves local searching capability by initialization. The experiments with simulated data and real IRIS data show that the clustering effect of the algorithm is better than that of PSO and KMEANS algorithm. |
| Key words: clustering analysis,PSO algorithm,initialization,KMEANS algorithm,data distribution |
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