As the basic concepts used in association rule learning are related to conditional probability and ratio to independence, I was wondering if Correspondence Analysis has been used in the literature with this. I understand the main motivation in association rule learning is efficiency in CPU time and memory usage but these days SVD (Singular Value Decomposition) is pretty fast and some algorithms can be very scarce in memory usage?
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MCA on binary data will lead into a continuous factorial space (admittedly with a much smaller dimensionality if strong correlations are present)
- to run AR on this space, you'll need to discretize the factorial space (not so easy)
- to intepret the AR results, you'll need to have first a good interpretation of your factors (not easy at all, except in textbooks, maybe)
one advantage of AR is that the results can directly be interpreted in the description space ; this is lost after MCA
however, MCA has been used together with AR so as to rank/select the "best" rules :
https://pdfs.semanticscholar.org/d386/1b793ecf7d98627424ad7ec49059810b3776.pdf
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