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学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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Balance K-means Algorithm
DOI码：10.1109/CISE.2009.5362578
所属单位：西南交通大学
发表刊物：International Conference on Computational Intelligence & Software Engineering
关键字：pattern clustering balance k-means algorithm clustering algorithm dataset feature values normalization standard K-means disadvantages Algorithm design and analysis Clustering algorithms Clustering methods Neural networks Partitioning algorithms
摘要：K-means is the most popular clustering algorithm and many researchers pay much attention to improving it. In this paper the authors find that some features influence so much on the results of clustering. For improving the K-means algorithm, the authors design a novel balance K-means algorithm. The main idea is that we normalize all the feature values of dataset before clustering. So all the features play the same important role in the clustering, which make the k-means balanced. There are three contributions to this paper. First the disadvantages of the standard K-means are illustrated in detail. Second we design the balance K-means algorithm which all the values of features are projected into a fix range, so it can take over the disadvantage of the standard K-means and. At last the authors choose some datasets from UCI for experiments. And the results of experiments show that the balance K-means runs better than the standard K-means.
合写作者：Jianhuai Qi, Weifan Zheng, Mingwen. Wang.
第一作者：Hongjun Wang
论文类型：学术论文
学科门类：工学
一级学科：计算机科学与技术
期号：12, 2009.
是否译文：否