An optimized initialization center K-means clustering algorithm based on density
文献类型：会议论文
| 作者 | Yuan QL(袁启龙); Shi HB(史海波) ; Zhou XF(周晓锋)
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| 出版日期 | 2015 |
| 会议名称 | 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) |
| 会议日期 | June 8-12, 2015 |
| 会议地点 | Shenyang, China |
| 关键词 | Clustering K-means Algorithm Initial Center Points Neighborhood Density Distance |
| 页码 | 790-794 |
| 中文摘要 | Traditional K-means algorithm's clustering effect is affected by the initial cluster center points. To solve this problem, a method is proposed to optimize the K-means initial center points. The algorithm use density-sensitive similarity measure to compute the density of objects. Through computing the minimum distance between the point and any other point with higher density, the candidate points are chosen out. Then, combined with the average density, the outliers are screened out. Ultimately the initial centers for K-means algorithm are screened out. Experimental results show that the algorithm gets the initial center points with high accuracy, and can effectively filter abnormal points. The running time and the iterations of the K-means algorithm are decreased obviously. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 1 |
| 会议录 | 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
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| 会议录出版者 | IEEE |
| 会议录出版地 | Piscataway, NJ, USA |
| 语种 | 英语 |
| ISSN号 | 2379-7711 |
| ISBN号 | 978-1-4799-8730-6 |
| WOS记录号 | WOS:000380502300150 |
| 源URL | [http://ir.sia.cn/handle/173321/18522]  |
| 专题 | 沈阳自动化研究所_数字工厂研究室 |
| 推荐引用方式 GB/T 7714 | Yuan QL,Shi HB,Zhou XF. An optimized initialization center K-means clustering algorithm based on density[C]. 见:2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Shenyang, China. June 8-12, 2015. |
入库方式： OAI收割
来源：沈阳自动化研究所
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