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| 一种基于密度的启发性群体智能聚类算法 | |
| 引用本文： | 陈云飞,刘玉树,钱越英,赵基海. 一种基于密度的启发性群体智能聚类算法[J]. 北京理工大学学报, 2005, 25(1): 45-48 |
| 作者姓名： | 陈云飞 刘玉树 钱越英 赵基海 |
| 作者单位： | 北京理工大学,信息科学技术学院计算机科学工程系,北京,100081;北京理工大学,信息科学技术学院计算机科学工程系,北京,100081;北京理工大学,信息科学技术学院计算机科学工程系,北京,100081;北京理工大学,信息科学技术学院计算机科学工程系,北京,100081 |
| 摘 要： | 提出一种基于密度的启发性群体智能聚类算法.针对以往群体智能聚类算法中分类错误率较高、算法运行时间较长等不足,提出记忆体方法和基于密度的先行(look ahead)策略.用人工数据集和真实数据集进行实验,将实验结果进行比较分析.分析结果表明,基于密度的启发性群体智能聚类算法能够得到令人满意的聚类结果,其分类错误率和运行时间明显小于其它聚类算法. |
| 关 键 词： | 密度 群体智能 聚类算法 |
| 文章编号： | 1001-0645(2005)01-0045-05 |
| 收稿时间： | 2004-04-05 |
A Heuristic Density-Based Clustering Algorithm of Swarm Intelligence |
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| CHEN Yun-fei,LIU Yu-shu,QIAN Yue-ying and ZHAO Ji-hai. A Heuristic Density-Based Clustering Algorithm of Swarm Intelligence[J]. Journal of Beijing Institute of Technology(Natural Science Edition), 2005, 25(1): 45-48 | |
| Authors: | CHEN Yun-fei LIU Yu-shu QIAN Yue-ying ZHAO Ji-hai |
| Affiliation: | Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Computer Science and Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China |
| Abstract: | A new heuristic density-based ant colony clustering algorithm (HDACC) is presented.The device of memory bank is first proposed, which brings forth the heuristic knowledge guiding ant to move in the bi-dimensional grid space. In this way, the algorithm's convergence is speeded up and the appearance of "un-assigned data object" avoided, and the error rate in classification drops subsequently. A density-based method is then proposed permiting each ant to look ahead and reduces the number of times in region-inquiry. Consequently the clustering time is saved. Some experiments were made on real and synthetic date sets. Experimental results were compared with those obtained using other classical clustering algorithms. The results demonstrated that the proposed HDBCSI is a viable and effective clustering algorithm. |
| Keywords: | density swarm intelligence clustering algorithm |
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