在线分裂聚类的半模糊的方法A Semi-fuzzy approach for online divisive-agglomerative clustering |
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课程网址: | http://videolectures.net/ecml07_pereiro_sfa/ |
主讲教师: | Pedro Pereira Rodrigues |
开课单位: | 波尔图大学 |
开课时间: | 2008-01-29 |
课程语种: | 英语 |
中文简介: | 在线划分-聚类分析 (odac) 是一种增量方法, 用于使用分层过程对流式程序进行时间序列的随时间变化进行分组。它使用基于流之间相关性的自上而下的策略, 构建类似树的流集群层次结构。该系统还具有聚集阶段, 以增强能够进行结构变化检测的动态行为。然而, 算法中使用的分割决策侧重于两组之间的清晰边界, 这意味着风险很高, 因为它必须只根据整个数据的一小部分来决定。在本文中, 我们提出了一种将变量分配给新创建的集群的半模糊方法, 以便在有效性和性能之间进行更好的权衡。实验工作支持我们的方法的好处。 |
课程简介: | The Online Divisive-Agglomerative Clustering (ODAC) is an incremental approach for clustering streaming time series using a hierarchical procedure over time. It constructs a tree-like hierarchy of clusters of streams, using a top-down strategy based on the correlation between streams. The system also possesses an agglomerative phase to enhance a dynamic behavior capable of structural change detection. However, the split decision used in the algorithm focus on the crisp boundary between two groups, which implies a high risk since it has to decide based on only a small subset of the entire data. In this work we propose a semi-fuzzy approach to the assignment of variables to newly created clusters, for a better trade-off between validity and performance. Experimental work supports the benefits of our approach. |
关 键 词: | 计算机科学; 机器学习; 聚类 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-03:张荧(课程编辑志愿者) |
阅读次数: | 40 |