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A hierarchical cluster algorithm based on binary model

机译:基于二进制模型的分层聚类算法

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It describes the similarity calculation method based on positive attribute distance, cluster evaluation criterion and the clustering process. The criterion mentioned in this alogrithm helps to measure the quality of each clustering level, and with the process of combinating each cluster, the level with the best quality can be seemed as the final cluster, while the cluster number of the level will be the best number. Several experiments on the UCI datasets prove the validity of our algorithm.
机译:描述了基于正属性距离,聚类评估标准和聚类过程的相似度计算方法。该算法中提到的标准有助于衡量每个聚类级别的质量,并且在组合每个聚类的过程中,具有最佳质量的级别可以看作是最终聚类,而该级别的聚类数将是最佳的数字。在UCI数据集上的几次实验证明了我们算法的有效性。

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