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DB-CSC：基于密度的子空间聚类与特征向量图的方法
DB-CSC: A density-based approach for subspace clustering in graphs with feature vectors |
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| 课程网址: |
http://videolectures.net/ecmlpkdd2011_gunnemann_vectors/
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| 主讲教师: |
Stephan Günnemann
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| 开课单位: |
亚琛工业大学
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| 开课时间: |
信息不详。欢迎您在右侧留言补充。
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| 课程语种: |
英语
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| 中文简介: |
表示属性信息和网络信息的数据源在当今的应用中广泛使用。为了充分发挥知识提取的潜力，集群之类的挖掘技术应该同时考虑这两种信息类型。最近的聚类方法将子空间聚类与密集子图挖掘相结合，以识别在属性子集中相似且在网络中紧密相连的对象组。虽然这些方法成功地规避了全空间聚类问题，但它们有限的聚类定义仅限于某些形状的聚类。在这项工作中，我们引入了一个基于密度的集群定义，考虑到子空间中的属性相似性和图密度。这种新的聚类模型使我们能够检测任意形状和大小的聚类。我们通过只选择最有趣的非冗余集群来避免结果中的冗余。在此基础上，介绍了基于该模型的聚类算法DB-CSC。在深入的实验中，我们通过与相关方法的比较，论证了DB-CSC的强度。
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| 课程简介: |
Data sources representing attribute information in combination with network information are widely available in today's applications. To realize the full potential for knowledge extraction, mining techniques like clustering should consider both information types simultaneously. Recent clustering approaches combine subspace clustering with dense subgraph mining to identify groups of objects that are similar in subsets of their attributes as well as densely connected within the network. While those approaches successfully circumvent the problem of full-space clustering, their limited cluster definitions are restricted to clusters of certain shapes. In this work, we introduce a density-based cluster definition taking the attribute similarity in subspaces and the graph density into account. This novel cluster model enables us to detect clusters of arbitrary shape and size. We avoid redundancy in the result by selecting only the most interesting non-redundant clusters. Based on this model, we introduce the clustering algorithm DB-CSC. In thorough experiments we demonstrate the strength of DB-CSC in comparison to related approaches.
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| 关 键 词: |
网络分析; 聚类分析 ; 计算机科学; 机器学习; 无监督学习
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-06：刘家豪(课程编辑志愿者)
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| 阅读次数: |
45
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