• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >K-MEAP: Multiple Exemplars Affinity Propagation With Specified K Clusters
【24h】

K-MEAP: Multiple Exemplars Affinity Propagation With Specified K Clusters

机译：K-MEAP：具有指定K簇的多个示例亲和力传播

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recently, an attractive clustering approach named multiexemplar affinity propagation (MEAP) has been proposed as an extension to the single exemplar-based AP. MEAP is able to automatically identify multiple exemplars for each cluster associated with a superexemplar. However, if the cluster number is a prior knowledge and can be specified by the user, MEAP is unable to make use of such knowledge directly in its learning process. Instead, it has to rely on rerunning the process as many times as it takes by tuning parameters until it generates the desired number of clusters. The process of MEAP rerunning may be very time-consuming. In this paper, we propose a new clustering algorithm called Multiple Exemplars Affinity Propagation with Specified K Clusters which is able to generate specified K clusters directly while retaining the advantages of MEAP. Two kinds of new additional messages are introduced in K-MEAP in order to control the number of clusters in the process of message passing. Detailed problem formulation, derived messages, and in-depth analysis of the proposed K-MEAP are provided. Experimental studies on 11 real-world data sets with different kinds of applications demonstrate that K-MEAP not only generates K clusters directly and efficiently without tuning parameters but also outperforms related approaches in terms of clustering accuracy.
机译：近来，已提出了一种称为多示例亲和力传播（MEAP）的有吸引力的聚类方法，作为对基于示例的单个AP的扩展。 MEAP能够为与超示例相关联的每个群集自动识别多个示例。但是，如果群集号是先验知识并且可以由用户指定，则MEAP无法在其学习过程中直接使用这些知识。取而代之的是，它必须尽可能多地重新运行该过程（通过调整参数），直到生成所需数量的群集为止。 MEAP重新运行的过程可能非常耗时。在本文中，我们提出了一种新的聚类算法，即带有指定K聚类的多样本亲和力传播，它能够直接生成指定的K聚类，同时保留MEAP的优势。为了控制消息传递过程中的簇数，在K-MEAP中引入了两种新的附加消息。提供了详细的问题表述，派生的消息以及对拟议的K-MEAP的深入分析。对11种具有不同应用程序的真实数据集的实验研究表明，K-MEAP不仅无需调整参数即可直接有效地生成K聚类，而且在聚类精度方面也优于相关方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号