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An Improved Clustering Algorithm and Its Application in WeChat Sports Users Analysis

机译:改进的聚类算法及其在微信体育用户分析中的应用

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Determining the number of clusters is an important issue in clustering, which can be either designated artificially or determined automatically. For the latter, it’s critical to design an appropriate method to update clusters number. Although many researches have been made for numerical, categorical or mixed datasets, most of them are not very effective or cannot guarantee the unique clustering result. To address these problems, an improved clustering algorithm based on entropy is put forward, which uses the divergence to determine the initial cluster centers and introduce the inter-cluster entropy for mixed data to update clusters number. The experiments on the 3 dataset in UCI and the practical dataset from WeChat sports users show that the improved algorithm is a deterministic clustering algorithm with good performance.
机译:确定群集的数量是群集中的重要问题,可以人工指定或自动确定。对于后者,设计适当的方法来更新簇数至关重要。尽管已经对数值,分类或混合数据集进行了许多研究,但大多数研究不是很有效,也不能保证唯一的聚类结果。针对这些问题,提出了一种基于熵的改进聚类算法,该算法利用散度确定初始聚类中心,引入混合数据的聚类间熵来更新聚类数。在UCI的3个数据集和微信体育用户的实际数据集上的实验表明,改进算法是一种性能良好的确定性聚类算法。

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