图的埃尔坎K-均值

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摘要翻译：
本文将K-均值算法从欧氏域推广到图的域。为了重新计算质心，我们应用次梯度方法来求解基于优化的图样本均值公式。为了加速图的k-means算法，而不以计算时间为代价，避免不必要的图距离计算，我们利用了Elkan在[cite{Elkan03}中提出的k-means算法的基础距离度量的三角不等式。实验表明，只要数据中存在簇结构，加速k-means算法比标准k-means算法更快。
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英文标题：
《Elkan's k-Means for Graphs》
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作者：
Brijnesh J. Jain and Klaus Obermayer
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper extends k-means algorithms from the Euclidean domain to the domain of graphs. To recompute the centroids, we apply subgradient methods for solving the optimization-based formulation of the sample mean of graphs. To accelerate the k-means algorithm for graphs without trading computational time against solution quality, we avoid unnecessary graph distance calculations by exploiting the triangle inequality of the underlying distance metric following Elkan's k-means algorithm proposed in \cite{Elkan03}. In experiments we show that the accelerated k-means algorithm are faster than the standard k-means algorithm for graphs provided there is a cluster structure in the data.
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PDF链接：
https://arxiv.org/pdf/0912.4598

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关键词：Intelligence Calculations Optimization Presentation Computation 公式 优化 distance paper 样本均值