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摘要: 众所周知, K-means (以下简称KM) 对初始点十分敏感. 本文提出了一种新的初始化KM 的方法, 它先估计出k个类的特征中心的位置, 然后用估计出的特征中心来初始化KM. 在人工数据集和真实数据集上的实验表明, 本文的方法所得到的结果要好于其他一些初始化KM 的方法.
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关键词:
- 聚类 /
- K-means 算法 /
- 特征中心
Abstract: It is well known that K-means algorithm (KM) is very sensitive to the initial conditions. In this paper, we propose a new method to initialize KM. It estimates the eigencenters of the k clusters, and initializes KM with these estimated eigencenters. Experiments on the artificial data set and the real data set show that our method significantly outperforms other initialization methods.-
Key words:
- Clustering /
- K-means algorithm /
- eigencenter
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