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一个原则性的、灵活的寻找替代集群的框架
A Principled and Flexible Framework for Finding Alternative Clusterings |
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| 课程网址: |
http://videolectures.net/kdd09_davidson_apaff/
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| 主讲教师: |
Ian Davidson
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| 开课单位: |
加利福尼亚大学
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| 开课时间: |
2009-09-14
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| 课程语种: |
英语
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| 中文简介: |
数据挖掘的目的是在数据中找到新颖且可操作的见解。然而，大多数算法通常只能找到单个（可能是非新颖/可操作的）数据解释，即使可能存在替代方案。在文献中很少关注找到给定原始聚类的替代方案的问题。当前的技术（包括我们以前的工作）没有聚焦/未定义，因为它们广泛地尝试找到替代聚类，但是没有指定应该或不应该保留原始聚类的哪些属性。在这项工作中，我们探索了一个有原则和灵活的框架，以便找到数据的替代聚类。该方法具有原则性，因为它构成了约束优化问题，因此可以理解其精确行为。它是灵活的，因为用户可以基于现有的聚类正式指定正反馈和负反馈，现有聚类的范围从哪些聚类保持（或不保持）在替代性和聚类质量之间进行权衡。
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| 课程简介: |
The aim of data mining is to find novel and actionable insights in data. However, most algorithms typically just find a single (possibly non-novel/actionable) interpretation of the data even though alternatives could exist. The problem of finding an alternative to a given original clustering has received little attention in the literature. Current techniques (including our previous work) are unfocused/unrefined in that they broadly attempt to find an alternative clustering but do not specify which properties of the original clustering should or should not be retained. In this work, we explore a principled and flexible framework in order to find alternative clusterings of the data. The approach is principled since it poses a constrained optimization problem, so its exact behavior is understood. It is flexible since the user can formally specify positive and negative feedback based on the existing clustering, which ranges from which clusters to keep (or not) to making a trade-off between alternativeness and clustering quality.
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| 关 键 词: |
数据挖掘; 替代聚类; 聚类保持
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-05-10：lxf
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| 阅读次数: |
24
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图 书 馆