• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The international journal of virtual reality >Abstracts of other papers accepted by DMDCM' 2011
【24h】

Abstracts of other papers accepted by DMDCM' 2011

机译：DMDCM'2011接受的其他论文摘要

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Clustering is one of most important buildinfelds in data mining and in machine learning in general. Most clustering algorithms is designed for off-line (or batch) processing, in which the clustering process repeatedly sweeps through the set of data samples in order to capture its underlying structure in a compact and efficient way. However, with the continuous increment of set of data samples, many recent applications require that the clustering algorithm should be online, or incremental in order to save time and improve efficiency of the algorithm. In this paper, an OPTICS based incremental clustering algorithm is put forward. It adopts reachability-polt to reflect the underlying structure of data sets. Through the experiment, it shows that the algorithm not only inherits the advantages of the classical OPTICS on clustering accuracy, but also possesses the functions of online clustering. Compared with the classical OPTICS, the proposed algorithm effectively reduces the time cost of clustering.
机译：总体而言，集群是数据挖掘和机器学习中最重要的构建要素之一。大多数聚类算法都是为离线（或批处理）处理而设计的，其中聚类过程会反复扫描数据样本集，以便以紧凑高效的方式捕获其底层结构。但是，随着数据样本集的不断增加，许多最新的应用程序要求聚类算法应该在线或递增，以节省时间并提高算法效率。本文提出了一种基于OPTICS的增量聚类算法。它采用可达性轮询来反映数据集的基础结构。通过实验表明，该算法不仅继承了经典OPTICS算法在聚类精度上的优势，还具有在线聚类的功能。与经典的OPTICS相比，该算法有效地降低了聚类的时间成本。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号