• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Fuzzy Systems >Fuzzy clustering Algorithm based on Adaptive City-block distance and Entropy Regularization
【24h】

Fuzzy clustering Algorithm based on Adaptive City-block distance and Entropy Regularization

机译：基于自适应城市距离和熵正则化的模糊聚类算法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The Euclidean distance is traditionally used to compare the objects and the prototypes in the Fuzzy C-Means algorithms, but theoretical studies indicate that methods based on City-Block distances are more robust concerning the presence of outliers in the dataset than those based on Euclidean distances. Moreover, most often conventional Fuzzy C-Means clustering algorithms consider that all variables are equally important for the clustering task. However, in real situations, some variables may be more or less relevant or even irrelevant for clustering. This paper proposes a partitioning fuzzy clustering algorithm based on Adaptive City-block distances and entropy regularization. The proposed method optimizes an objective function by alternating three steps aiming to compute the fuzzy cluster representatives, the fuzzy partition, as well as relevance weights for the variables. Several experiments on synthetic and real-world datasets including its application to noisy image texture segmentation are presented to corroborate both clustering and robustness capabilities of the proposed algorithm over conventional approaches.
机译：传统上，欧氏距离用于比较Fuzzy C-Means算法中的对象和原型，但是理论研究表明，基于城市块距离的方法在数据集中存在离群值要比基于欧氏距离的方法更健壮。此外，最常见的常规模糊C均值聚类算法认为所有变量对于聚类任​​务都同样重要。但是，在实际情况下，某些变量可能或多或少与聚类相关，甚至不相关。提出了一种基于自适应城市块距离和熵正则化的划分模糊聚类算法。所提出的方法通过交替三个步骤来优化目标函数，目的是计算模糊聚类代表，模糊分区以及变量的相关权重。提出了一些关于合成数据集和真实世界数据集的实验，包括其在嘈杂图像纹理分割中的应用，以证实该算法在传统方法上的聚类和鲁棒性。

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