How cluster a twitter data-set? I have a twitter data-set and I wanna extract their related topics. So, I decided to classify my Tweets into clusters using an unsupervised machine learning algorithm like k-means. This choice is made due the time consuming of the training process in the supervised approaches.

So, as a first step after cleaning my tweets, I will extract features (eg. Hashtags...) from them, and enrich them with side information from knowledge bases (eg. Wikipedia). Secondly, they will be represented in a Vector space. Next, using k-means and for given K=6 clusters, my tweets already enriched will be classified into 6 clusters.

However, I don’t know how to identify automatically the topics related to these clusters. Is there any solutions?
k-means is very sensitive to noise

because it is designed as a least-squares approach. Noise deviations, when squared, become even larger.

Twitter is mostly noise

Twitter is full of spam and nonsense tweets. These will be entirely unlike any other and thus have the largest deviations.

Chances are you get one "cluster" that contains almost everything, and the other k-1 clusters consist of a few tweets with their duplicates. Clusters are not topics. They are more likely to be duplicates than topics.

An appropriate clustering algorithm for tweets should probably discard 90% of tweets and produce thousands of clusters. But it will rarely be better than finding all tweets in common - most tweets only have 2-3 usable words.