| 基于密度加权的粗糙K-均值聚类改进算法 |
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| 引用本文： | 郑超,苗夺谦,王睿智.基于密度加权的粗糙K-均值聚类改进算法[J].计算机科学,2009,36(3):220-222. |
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| 作者姓名： | 郑超 苗夺谦 王睿智 |
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| 作者单位： | 同济大学计算科学与技术系,上海,201804 |
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| 基金项目： | 国家自然科学基金，高等学校博士学科点专项科研基金 |
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| 摘 要： | 针对粗糙K-均值聚类算法中类均值计算式的特点,提出了一种改进的粗糙K-均值算法.改进后的算法基于数据对象所在区域的密度,在类的均值计算过程中对每个对象赋以不同的权重.不同测试数据集的实验结果表明,改进后的粗糙K-均值算法提高了聚类的准确性,降低了迭代次数,并且可以有效地减小孤立点对聚类的影响.
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| 关 键 词： | 聚类算法 粗糙K-均值 密度 孤立点 |
| 收稿时间： | 2008/4/28 0:00:00 |
Improved Rough K-means Clustering Algorithm with Weight Based on Density |
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| ZHENG Chao,MIAO Duo-qian,WANG Rui-zhi.Improved Rough K-means Clustering Algorithm with Weight Based on Density[J].Computer Science,2009,36(3):220-222. |
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| Authors: | ZHENG Chao MIAO Duo-qian WANG Rui-zhi |
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| Affiliation: | Department of Computer Science and Technology;Tongji University;Shanghai 201804;China |
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| Abstract: | According to the feature of the calculation of means in Rough K-means algorithm, an improved Rough K- means algorithm was proposed. The new algorithm introduces weights to the calculation of means, which is based on the density of each point. The experime |
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| Keywords: | Clustering algorithm Rough K-means Density Outlier |
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