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I would like to clarify the relationship between latent Dirichlet allocation (LDA) and the generic task of document clustering.
The LDA analysis tends to output the topic proportions for each document. If my understanding is correct, this is not the direct result of document clustering. However, we can treat this probability proportions as a feature reprsentation for each document. Afterwards, we can invoke other established clustering method based on the feature configurations generated by LDA analysis.
Is my understanding correct? Thanks.
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Strictly speaking, Latent Dirichlet Allocation (LDA) is not a clustering algorithm. This is because clustering algorithms produce one grouping per item being clustered, whereas LDA produces a distribution of groupings over the items being clustered.
LDA looks at a document to determine a set of topics that are likely to have generated that collection of words. So, if a document uses certain words that are contained in a topic, you could say the document is about that topic.
LDA (Latent Dirichlet Allocation) is a soft clustering algorithm that is ideal for text [5] but also for other purposes such as ge- netics [21] where a relationship between a gene and a genotype can be considered similar to a relationship between a word and a document.
Though the name is a mouthful, the concept behind this is very simple. To tell briefly, LDA imagines a fixed set of topics. Each topic represents a set of words. And the goal of LDA is to map all the documents to the topics in a way, such that the words in each document are mostly captured by those imaginary topics.
Yes, you can treat the output of LDA as features for your documents; this is exactly what Blei, Ng and Jordan did in the paper that introduced LDA. They did it for classification, but for clustering the procedure is the same.
(In machine learning terminology, this use of LDA is called dimensionality reduction because it reduces the feature space's number of dimensions from |V|, the vocabulary size, to some number k of topics selected by the user.)
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