Discovering interesting itemsets using hierarchical Indian buffet process latent feature models
by Abhinav Pandey
M.Phil. Informational Systems, Business Statistics and Operations Management
1 online resource (viii, 23 pages)
We approach the classic problem of discovering interesting itemsets in data using an
infinite latent feature model that utilizes novel constructions of the Hierarchical Indian
Buffet Process (HIBP). These constructions yield explicit expressions for the posterior,
predictive and marginal distributions of the HIBP which we utilize to obtain likelihood
and predictive distributions for our model resulting in an implementation that was
unavailable in literature. We propose an extension to using a Poisson HIBP to allow for
random count valued latent features and derive a predictive distribution for that model.
We also provide a sequential sampling scheme for our model which allows sequential
sampling of items by existing customers and new customers.
Permanent URL for this record: https://lbezone.hkust.edu.hk/bib/991013049929103412
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