计算机科学 ›› 2011, Vol. 38 ›› Issue (3): 218-221.
• 人工智能 • 上一篇 下一篇
王丹,吴孟达
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WANG Dan,WU Meng-da
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摘要: 粗糙C均值算法中3个参数wz,wμ,ε的选择是算法应用的关键问题。针对粗糙C均值算法中反映类间叠加程度的参数:的设定,提出一种动态自适应调整阂值。的粗糙C均值算法,该算法根据“类一类”间距离与“对象一类”间距离,对每一个待聚类对象动态设定阂值:。两组人工数据和图像数据的实验表明,该算法具有较好的适应性和聚类效果。
关键词: C均值聚类,粗糙集,粗糙C均值聚类
Abstract: Selection of parameters wz,wμ,εplays an important role in rough C; Means algorithm In this paper, a dynamic threshold rough C-Means algorithm was proposed to self-adaptive adjusting threshold。that reflects the superposition between classes. This algorithm computes a threshold for every object on the basis of class interval and the distance between class and o场ect, The better effect can be testified by two synthetic data and image data experiments.
Key words: C-Means clustering, Rough sets, Rough C-Means clustering
王丹,吴孟达. 动态阈值粗糙C均值算法[J]. 计算机科学, 2011, 38(3): 218-221. https://doi.org/
WANG Dan,WU Meng-da. Dynamic Threshold Rough C-Means Algorithm[J]. Computer Science, 2011, 38(3): 218-221. https://doi.org/
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