摘要翻译：
亲和传播聚类(AP)有两个局限性：很难知道参数“偏好”的取值可以得到最优的聚类解；如果发生振荡，不能自动消除。为了克服这些缺陷，提出了自适应AP方法，包括自适应扫描偏好以搜索簇数空间来寻找最优聚类解，自适应调整阻尼因子以消除振荡，以及当阻尼调整技术失败时自适应逃避振荡。在模拟和真实数据集上的实验结果表明，自适应聚类算法是有效的，在聚类结果质量上优于自适应聚类算法。
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英文标题：
《Adaptive Affinity Propagation Clustering》
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作者：
Kaijun Wang, Junying Zhang, Dan Li, Xinna Zhang and Tao Guo
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最新提交年份：
2008
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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：
  Affinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter 'preference' can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clustering solution, adaptive adjustment of damping factors to eliminate oscillations, and adaptive escaping from oscillations when the damping adjustment technique fails. Experimental results on simulated and real data sets show that the adaptive AP is effective and can outperform AP in quality of clustering results.
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PDF链接：
https://arxiv.org/pdf/0805.1096