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首页> 外文期刊>International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems >Consensus Function Based on Clusters Clustering and Iterative Fusion of Base Clusters
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Consensus Function Based on Clusters Clustering and Iterative Fusion of Base Clusters

机译：基于聚类聚类和基础聚类迭代融合的共识函数

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摘要

In clustering ensemble, it is desired to combine several clustering outputs in order to create better results than the output results of the basic individual clustering methods in terms of consistency, robustness and performance. In this research, we want to present a clustering ensemble method with a new aggregation function. The proposed method is named Robust Clustering Ensemble based on Iterative Fusion of Base Clusters (RCEIFBC). This method takes into account the two similarity criteria: (a) one of them is the cluster-cluster similarity and (b) the other one is the object-cluster similarity. The proposed method has two steps and has been done on the binary cluster representation of the given ensemble. Indeed, before doing any step, the primary partitions are converted into a binary cluster representation where the primary ensemble has been broken into a number of primary binary clusters. The first step is to combine the primary binary clusters with the highest cluster-cluster similarity. This phase will be replicated as long as our desired candidate clusters are ready. The second step is to improve the merged clusters by assigning the data points to the merged clusters. The performance and robustness of the proposed method have been evaluated over different machine learning datasets. The experimentation indicates the effectiveness of the proposed method comparing to the state-of-the-art clustering methods in terms of performance and robustness.
机译：在聚类合奏中，期望在一致性，鲁棒性和性能方面组合多个聚类输出以产生比基本单个聚类方法的输出结果更好的结果。在这项研究中，我们想提出一种具有新聚合功能的聚类集成方法。该方法被称为基于基本聚类的迭代融合的鲁棒聚类集成（RCEIFBC）。此方法考虑了两个相似性标准：（a）其中一个是集群-群集相似性，（b）另一个是对象-群集相似性。所提出的方法有两个步骤，并且已经在给定集合的二进制聚类表示上完成。实际上，在执行任何步骤之前，主要分区都已转换为二进制群集表示形式，其中主要集合已分解为多个主要二进制群集。第一步是将具有最高群集群集相似性的主要二进制群集组合在一起。只要我们所需的候选群集已准备就绪，此阶段将被复制。第二步是通过将数据点分配给合并的群集来改进合并的群集。已经在不同的机器学习数据集上评估了该方法的性能和鲁棒性。实验表明，与最新的聚类方法相比，该方法在性能和鲁棒性方面是有效的。

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