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I'm new to these fields of Machine Learning, and I'll have to use unsupervised clustering of texts, that is make two clusters of words from a document, but without using the widely used K-Means Clustering. I understand there are lots of other algorithms, but what could be the best unsupervised algorithmic choice for such a task, apart from K-Means ?

Thanks in advance ...

Helen07
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  • Hi Helen, welcome to CV! At the moment, your question is a bit too broad: What do you mean by "best algorithmic choice"? Are you just looking for alternatives to K-means? What other algorithms have you considered so far? Try narrowing the question and show your research efforts up to now to increase the chance of getting an answer. – Jan Kukacka May 21 '18 at 08:14
  • Thanks Jan for the reply.. I was just looking for alternatives, such as Unsupervised Random Forest / Autoencoders / Hebbian Learning / Generative Adversial Networks / DBSCAN / Unsupervised Binary Trees / Fuzzy C Means or any other unsupervised algos.. – Helen07 May 21 '18 at 23:37
  • Great! Do you have a more specific question regarding any of those? If so, consider editing the question text rather than posting additional info in the comments section where it is easy to overlook. – Jan Kukacka May 22 '18 at 06:48
  • Well I guess my focus is more into from the algorithms that I've mentioned, which one could be used for Document Clustering? yes I'm more going into the direction of Document Clustering but without using K-Means clustering. – Helen07 May 23 '18 at 11:54

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