哪种聚类方法适合哪种数据？
我想知道
- K-means 最适合哪种类型的数据聚类？
- 当 k 均值失败时？对于哪种类型的数据集，k-means 不能给出准确的答案？
- COBWEB 最适合哪种类型数据的集群？
- COBWEB何时失败？对于哪些类型的数据集COBWEB没有给出准确的答案？
I would like to know
- K-means is best suited for clustering of which type of data?
- When k-means fails? for which type of data set k-means does not give accurate answer?
- COBWEB is best suited for clustering of which type of data?
- When COBWEB fails? for which type of data set COBWEB does not give accurate answer?
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1）看看一些无限训练有限训练，我可以说K-means最适合任何类型的可以分为向量的数据，并且最适合定量数据。
2）K-means失败当数据数量不是很多时，当初始条件敏感或闪烁时，会给出不同的结果
1)Looking at some Infinite training Finite training, I can say that K-means is best suited for any kind of data which can be divided in to vectors and best for quantitative data.
2)K-means fails When the numbers of data are not so many,When initial condition is sensitive or flickering ,which gives different results
当使用欧几里德距离时，K 均值在高维中可能会出现问题，因为所有内容最终都会“接近”。
您想要进行什么类型的聚类？
K-means can have issues in high dimensions when using euclidian distance as everything ends up being "close".
What type of clustering are you trying to do?
我在使用 K 均值聚类包含倾角和倾角方向（球体表面上的点）的数据集时遇到问题。最后我不得不创建一个粗略的遗传算法来进行聚类。
I have had problems using K means clustering with a data set that included Dip and Dip Direction (points on the surface of a sphere). In the end I had to create a crude genetic algorithm to do the clustering.