Python 如何从K-均值聚类中解释轮廓系数？

我正在使用sklearn包练习K-Means聚类。 我正在使用示例购物数据集，其中包括每个客户在每个项目类别（即食品、时尚、数字等）上的花费

共有42个特征，这意味着我曾将42个项目类别输入K-Means。当我在2-50之间检查k的轮廓系数时，结果如下所示：

结果

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我不知道如何利用这个结果。在我看来，随着我的前进，s不断变大。我这样做对吗？或者我应该尝试另一种群集评估方法吗？

点的轮廓测量点与其群集相对于下一个最近群集的相似程度。这是距离到簇中心的比率，标准化为“1”与其簇完全匹配，-1”与簇完全不匹配

（注：聚类中心的使用可能是k-均值聚类特有的。）

集群的轮廓是其所有成员的平均轮廓。这意味着实践是，较大的数字意味着集群与其其他集群“分离”

我认为轮廓是沿着簇的边界测量点的密度。当轮廓较高时，边界上的点很少。这就是你想要的——分离良好的集群

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当使用k-means时，小的“异常值”簇通常会有大的轮廓。通常较大的星团有密集的边界。您可以看看它的尺寸和轮廓。

谢谢。所以对于我得到的结果，49个簇比2个簇好。这意味着有了49个星团，它与其他星团的距离就更大了。我说的对吗？@2D\uu。嗯，你必须以不同的方式评估集群。如果每个点都有一个单独的簇，那么我认为轮廓看起来会很好（我不能100%确定退化情况下会发生什么）。更重要的是：集群有用吗？你是对的。我想你可能是对的。我当然不想要太多的集群。我将研究集群，并确定哪个数字最有意义。谢谢

For n_clusters=2, The Silhouette Coefficient is 0.296883351294 
For n_clusters=3, The Silhouette Coefficient is 0.429716008727
For n_clusters=4, The Silhouette Coefficient is 0.5379833453
For n_clusters=5, The Silhouette Coefficient is 0.640200087198
For n_clusters=6, The Silhouette Coefficient is 0.720988889121
For n_clusters=7, The Silhouette Coefficient is 0.754509135746
For n_clusters=8, The Silhouette Coefficient is 0.824498184042
For n_clusters=9, The Silhouette Coefficient is 0.859505132529
For n_clusters=10, The Silhouette Coefficient is 0.886719390512
For n_clusters=11, The Silhouette Coefficient is 0.909094073152
For n_clusters=12, The Silhouette Coefficient is 0.924484657787
For n_clusters=13, The Silhouette Coefficient is 0.935920328988
For n_clusters=14, The Silhouette Coefficient is 0.941202266924
For n_clusters=15, The Silhouette Coefficient is 0.944696312832
For n_clusters=16, The Silhouette Coefficient is 0.94973283735
For n_clusters=17, The Silhouette Coefficient is 0.953130541493
For n_clusters=18, The Silhouette Coefficient is 0.956455183621
For n_clusters=19, The Silhouette Coefficient is 0.959253033224
For n_clusters=20, The Silhouette Coefficient is 0.962360042108
For n_clusters=21, The Silhouette Coefficient is 0.964250208432
For n_clusters=22, The Silhouette Coefficient is 0.967326417612
For n_clusters=23, The Silhouette Coefficient is 0.969331109452
For n_clusters=24, The Silhouette Coefficient is 0.971127562002
For n_clusters=25, The Silhouette Coefficient is 0.972261973972
For n_clusters=26, The Silhouette Coefficient is 0.9734445716
For n_clusters=27, The Silhouette Coefficient is 0.974238560202
For n_clusters=28, The Silhouette Coefficient is 0.97488260729
For n_clusters=29, The Silhouette Coefficient is 0.97531193231
For n_clusters=30, The Silhouette Coefficient is 0.974524792419
For n_clusters=31, The Silhouette Coefficient is 0.975612314038
For n_clusters=32, The Silhouette Coefficient is 0.975737449165
For n_clusters=33, The Silhouette Coefficient is 0.976396323376
For n_clusters=34, The Silhouette Coefficient is 0.977655049988
For n_clusters=35, The Silhouette Coefficient is 0.977653124893
For n_clusters=36, The Silhouette Coefficient is 0.977692656935
For n_clusters=37, The Silhouette Coefficient is 0.977631627533
For n_clusters=38, The Silhouette Coefficient is 0.978547753839
For n_clusters=39, The Silhouette Coefficient is 0.978886776953
For n_clusters=40, The Silhouette Coefficient is 0.979381767137
For n_clusters=41, The Silhouette Coefficient is 0.9796349521
For n_clusters=42, The Silhouette Coefficient is 0.979461929477
For n_clusters=43, The Silhouette Coefficient is 0.980920963377
For n_clusters=44, The Silhouette Coefficient is 0.980129624336
For n_clusters=45, The Silhouette Coefficient is 0.981374785468
For n_clusters=46, The Silhouette Coefficient is 0.980656482976
For n_clusters=47, The Silhouette Coefficient is 0.982323770297
For n_clusters=48, The Silhouette Coefficient is 0.982538183341
For n_clusters=49, The Silhouette Coefficient is 0.982842003856