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Hybrid Clustering Algorithm

机译:混合聚类算法

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摘要

The paper presents a new graph based clustering algorithm. Traditional clustering algorithms have the drawback that it takes large number of iterations in order to come up with the desired number of clusters. The advantage of this approach is that the size of the dataset is reduced using graph based clustering approach and the required number of clusters is generated using K means algorithm. The proposed algorithm consists of two phases, the first phase being constructing the graph and de-associating the graphs into connected sub graphs which denote the number of sub groups within the data. In the second phase in order to group the sub graphs that are close to each other K means algorithm is employed.
机译:本文提出了一种基于族的群集算法。传统的聚类算法具有大量迭代的缺点,以便提出所需的群集。这种方法的优点是使用基于曲线图的聚类方法来减少数据集的大小,并且使用K表示算法生成所需的簇数。所提出的算法由两个阶段组成,第一阶段正在构成图形并将图形解到连接到连接的子图中,该曲线图在连接数据中表示子图的数量。在第二阶段中,为了将靠近彼此接近的子图来分组k表示算法。

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