依赖焦点的多层次图聚类

François Boutin, Mountaz Hascoët
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引用次数: 15

摘要

本文提出了一种基于结构的聚类技术,该技术将给定的图转换为特定的双树结构,称为多级轮廓树。树的每个元节点——代表节点的一个子集——本身是分层聚集的。因此,元节点被视为包含集群的树的根。我们的方法的主要独创性在于在聚类过程中考虑到用户的关注点,从而从不同的角度提供视图。多层轮廓树在线性时间内计算,易于探索。我们认为我们的技术非常适合调查各种图表,如Web图表或引用图表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Focus dependent multi-level graph clustering
In this paper we propose a structure-based clustering technique that transforms a given graph into a specific double tree structure called multi-level outline tree. Each meta-node of the tree - that represents a subset of nodes - is itself hierarchically clustered. So, a meta-node is considered as a tree root of included clusters.The main originality of our approach is to account for the user focus in the clustering process to provide views from different perspectives. Multi-level outline trees are computed in linear time and easy to explore. We think that our technique is well suited to investigate various graphs like Web graphs or citation graphs.
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