基于medoids的证据关系聚类
Evidential relational clustering using medoids
摘要：
在实际的聚类应用中，接近度数据，其中只有成对的相似性或不相似性是已知的，是更一般的对象数据，其中每个模式是明确描述的属性列表。基于Medoid的聚类算法假设类的原型是对象，对于关系数据集的划分具有重要价值。本文提出了一种新的基于原型的聚类方法--证据C中心点（ECMdd），它是模糊C中心点（FCMDD）在信任函数理论框架上的扩展。在ECMdd中，medoids被用作原型来表示检测到的类，包括特定类和不精确类。特定类是用于那些明显远离其他类的原型的数据，而不精确类接受那些可能接近多个类的原型的对象。这种软决策机制可以使聚类结果更加谨慎，降低误分类率。合成和真实数据集的实验被用来说明ECMdd的性能。结果表明，ECMdd能很好地捕捉内部数据结构中的不确定性。此外，它是更强大的初始化相比，FCMDD。
英文摘要：
In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets. In this paper a new prototype-based clustering method, named Evidential C-Medoids (ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions is proposed. In ECMdd, medoids are utilized as the prototypes to represent the detected classes, including specific classes and imprecise classes. Specific classes are for the data which are distinctly far from the prototypes of other classes, while imprecise classes accept the objects that may be close to the prototypes of more than one class. This soft decision mechanism could make the clustering results more cautious and reduce the misclassification rates. Experiments in synthetic and real data sets are used to illustrate the performance of ECMdd. The results show that ECMdd could capture well the uncertainty in the internal data structure. Moreover, it is more robust to the initializations compared with FCMdd.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
