IMPACT: A Novel Clustering Algorithm based on Attraction
IMPACT: A Novel Clustering Algorithm based on Attraction
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DOI：
10.4304/jcp.7.3.653-665
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发表时间：
2012-01
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通讯作者：
Vu Anh Tran;J. Clemente;Duc Thuan Nguyen;Jiuyong Li;Xuan Tho Dang;Thi Tu Kien Le;Thi Lan Anh Nguyen;Thammakorn Saethang;Mamoru Kubo;Yoichi Yamada;K. Satou
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文献类型：
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作者：
Vu Anh Tran;J. Clemente;Duc Thuan Nguyen;Jiuyong Li;Xuan Tho Dang;Thi Tu Kien Le;Thi Lan Anh Nguyen;Thammakorn Saethang;Mamoru Kubo;Yoichi Yamada;K. Satou
集群是一个发现过程，它将数据对象分组到集群中，从而最大化集群内的相似性并最小化集群间的相似性。本文提出了一种新颖的聚类算法Impact(基于对聚类数据的吸引力迭代移动点)，该算法通过根据数据对象的吸引力将其移动到更近的位置来对数据对象进行划分。这些移动增加了集群之间的分离，同时保留了数据的全局结构。我们的算法不需要簇的数量或其他参数的先验说明来识别底层的簇结构。实验结果表明，对于包含不同聚类形状、密度、大小和噪声的数据集，与其他聚类算法相比，该算法具有更好的性能。
Clustering is a discovery process that groups data objects into clusters such that the intracluster similarity is maximized and the intercluster similarity is minimized. This paper proposes a novel-clustering algorithm, IMPACT (Iteratively Moving Points based on Attraction to ClusTer data), that partitions data objects by moving them closer according to their attractive forces. These movements increase separation among clusters while retaining the global structure of the data. Our algorithm does not require a priori specification of the number of clusters or other parameters to identify the underlying clustering structure. Experimental results show improvements over other clustering algorithms for datasets containing different cluster shapes, densities, sizes, and noise.