随机聚类算法 [英] Random Clustering Algorithm

问题描述

我已经设置点，我想簇了出来。我知道该怎么做普通K-means算法。但我不希望采取K'作为输入。假设，如果我有像点 1,3,4,50,60,70,1000,10002,10004算法应聚集成3群 C1：1,3,4 C2：50,60,70 C3：1000,1002,1004 群集内元件之间满意的距离应在最低限度，和簇间的距离应为最大

I have set of points, and i want clusters out of them. I know how to do normal k-means algorithm. But i don't want to take 'k' as input. Suppose if i have points like 1,3,4,50,60,70,1000,10002,10004 the algorithm should cluster them into 3 clusters C1: 1,3,4 C2: 50,60,70 C3: 1000,1002,1004 satisfying distance between intracluster elements should be minimum, and distance between intercluster should be maximum.

推荐答案

决定k是这重演了许多聚类算法的一个问题。你可能要考虑谱聚类（及其各种算法的表兄弟），它管理的一些缓解这一问题。然而，许多版本使用k均值作为最后一步，并返回到原点（但不是全部）。

Deciding on k is a problem which repeats itself with many clustering algorithms. You might want to consider spectral clustering (and its various algorithmic cousins) which manages to some alleviate that problem. However, many versions use k-means as the final step, returning you to square one (although not all).

替代地，有许多方法用于查找k的最优值，例如通过Denis上面提供的答案;这可能是足以让你的目的。

Alternatively, there are many approaches for finding the optimal value of k, such as the answer supplied by Denis above; this might be enough for your purposes.

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