基于蚁群算法的三支k-means聚类算法
Three-way k-means clustering based on ant colony
朱金 1徐天杰 2王平心3
作者信息
- 1. 江苏科技大学 经济与管理学院 镇江 212100
- 2. 江苏科技大学 计算机学院 镇江 212100
- 3. 江苏科技大学 理学院 镇江 212100
- 折叠
摘要
在聚类分析中,三支k-means聚类算法较具有较强的处理边界不确定数据的能力,但仍然存在对初始聚类中心敏感的问题.通过将蚁群算法和三支k-means聚类算法相结合,给出了一种基于蚁群算法的三支k-means聚类算法来解决这一问题.利用蚁群算法中随机概率选择策略和信息素的正负反馈机制,动态调整权重的方法,对三支k-means聚类算法进行优化.在UCI数据集上实验证明,该方法对聚类结果的性能指标有所提高.
Abstract
In the clustering analysis,three-way k-means clustering algorithm has a great improvement over the traditional k-means clustering algorithm.The algorithm has a strong ability to deal with data with uncertain boundary.However,it is still sensitive to the initial clustering center.By combining ant colony algorithm and three-way k-means clustering algorithm,this paper presents a three-way k-means clustering algorithm based on ant colony algorithm to solve this problem.Using the random probability selection strategy in ant colony algorithm and the positive and negative feedback mechanism of pheromone,the weight is dynamically adjusted to optimize the three k-means clustering algorithms.Experiments show that this method improves the performance index of clustering results.The effectiveness of the algorithm is verified on UCI data set.
关键词
三支k-means/k-means聚类算法/聚类中心/蚁群算法Key words
three-way k-means/k-means clustering/cluster center/ant colony引用本文复制引用
基金项目
国家自然科学基金项目(62076111)
国家自然科学基金项目(61773012)
江苏省高校自然科学基金项目(15KJB110004)
出版年
2024
NETL
NSTL
万方数据