My scenario is pretty straightforward: I have a bunch of news articles (~1k at the moment) for which I know that some cover the same story/topic. I now would like to group these articles based on shared story/topic, i.e., based on their similarity.
What I did so far is to apply basic NLP techniques including stopword removal and stemming. I also calculated the tf-idf vector for each article, and with this can also calculate the, e.g., cosine similarity based on these tf-idf-vectors. But now with the grouping of the articles I struggles a bit. I see two principle ways -- probably related -- to do it:
1) Machine Learning / Clustering: I already played a bit with existing clustering libraries, with more or less success; see here. On the one hand, algorithms such as k-means require the number of clusters as input, which I don't know. Other algorithms require parameters that are also not intuitive to specify (for me that is).
2) Graph algorithms: I can represent my data as a graph with the articles being the nodes and weighted adges representing the pairwise (cosine) similarity between the articles. With that, for example, I can first remove all edges that fall below a certain threshold and then might apply graph algorithms to look for strongly-connected subgraphs.
In short, I'm not sure where best to go from here -- I'm still pretty new in this area. I wonder if there some best practices for that, or some kind of guidelines which methods / algorithms can (not) be applied in certain scenarios.