图 书 馆
非齐次数据的依赖聚类与异聚类的统一Unifying Dependent Clustering and Disparate Clustering for Non-homogeneous Data |
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| 课程网址: | http://videolectures.net/kdd2010_hossain_udcd/ |
| 主讲教师: | M. Shahriar Hossain |
| 开课单位: | 弗吉尼亚理工学院 |
| 开课时间: | 2010-11-15 |
| 课程语种: | 英语 |
| 中文简介: |  现代数据挖掘设置涉及实体上属性值描述符的组合以及这些实体之间的指定关系。我们提出了一种通过使用关系施加相依的聚类或完全不同的聚类约束来聚类此类非齐次数据集的方法。与先前的将约束视为布尔标准的工作不同，我们提出了一种允许平滑地满足或违反约束的公式。这使我们能够通过仅最大化和最小化目标函数，使用相同的优化框架来实现依赖聚类和异构聚类。我们在合成数据以及一些现实世界的数据集上都给出了结果。 |
| 课程简介: | Modern data mining settings involve a combination of attribute-valued descriptors over entities as well as specified relationships between these entities. We present an approach to cluster such non-homogeneous datasets by using the relationships to impose either dependent clustering or disparate clustering constraints. Unlike prior work that views constraints as boolean criteria, we present a formulation that allows constraints to be satisfied or violated in a smooth manner. This enables us to achieve dependent clustering and disparate clustering using the same optimization framework by merely maximizing versus minimizing the objective function. We present results on both synthetic data as well as several real-world datasets. |
| 关 键 词: | 数据挖掘; 数据合成; 目标函数 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-07：zyk |
| 最后编审: | 2021-03-10：zyk |
| 阅读次数: | 30 |