How to compare different similarity measurements in text clustering?
I got the results of top n most similar titles, but all the results seem very reasonable, how can I compare them to see which method performs best?


I'm afraid there's no meaningful definition of "best" unless you specify some concrete metric.  If the results in each case seem reasonable, then all you can do is be educated about the various distance metrics and decide which makes the most sense for your use-case.  (For example, Euclidean distance measures spatial difference, while cosine similarity measures directional difference.)


  I also tried to do k-means, when I implement K-means clustering, it used Euclidean distance by default, how to use other distance to implement K-means?


Keep in mind that k-means is designed for Euclidean distance, which is why many implementations (such as scikit-learn) do not allow the use of any other distance function.  For many other distance metrics, such as cosine similarity, taking the mean of the points in the cluster is not appropriate, so you'd also need to replace the centroid-estimation function as well.

If you really want to, then the K-Means implementation from NLTK allows you to specify a custom distance metric.  Just keep in mind the GIGO principle.  If you violate the assumptions of the model, don't be surprised if you get poor results.