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I have read through the scikit learn documentation and Googled to no avail. I have 2000 data sets, clustered as the picture shows. Some of the clusters, as shown, are wrong, here the red cluster. I need a metrics to method to validate all the 2000 cluster-sets. Almost every metric in scikit learn requires the ground truth class labels, which I do not think I have or CAN have for that matter. I have the hourly traffic flow for 30 days and I am clustering them using k-means. The lines are the cluster centers. What should I do? Am I even on the right track?

enter image description here

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  • $\begingroup$ It will be hard for someone to help you without more information. What do your axes represent? You stated the lines are the cluster centers. So you ran k-means with k=3? $\endgroup$ – bogatron Jul 15 '14 at 22:41
  • $\begingroup$ Correct, the horizontal axis is the hour, 0 to 23, and the vertical is the traffic flow, so the dots you see are the traffic flow in that hour over the 30 days, so to say each hour should have 30 days of hourly flow data point. $\endgroup$ – plumSemPy Jul 15 '14 at 22:44
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SciKit learn has no methods, except from the silhouette coefficient, for internal evaluation, to my knowledge, we can implement the DB Index (Davies-Bouldin) and the Dunn Index for such problems. The article here provides good metrics for k-means:

http://www.iaeng.org/publication/IMECS2012/IMECS2012_pp471-476.pdf

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