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We consider here the well-known \u2018market basket\u2019 problem that is often discussed in the data mining community, and is also quite ubiquitous in biomedical research. The data under consideration are a set of \u2018baskets\u2019, where each basket contains a list of \u2018items\u2019. Our goal is to discover \u2018themes\u2019, which are defined as subsets of items that tend to co-occur in a basket. We describe a generative model, i.e. the theme dictionary model, for such data structures and describe two likelihood-based methods to infer themes that are hidden in a collection of baskets. We also propose a novel sequential Monte Carlo method to overcome computational challenges. Using both simulation studies and real applications, we demonstrate that the new approach proposed is significantly more powerful than existing methods, such as association rule mining and topic modelling, in detecting weak and subtle interactions in the data.<\/jats:p>","DOI":"10.1111\/rssb.12032","type":"journal-article","created":{"date-parts":[[2013,10,8]],"date-time":"2013-10-08T13:29:04Z","timestamp":1381238944000},"page":"319-347","source":"Crossref","is-referenced-by-count":7,"title":["Association Pattern Discovery Via Theme Dictionary Models"],"prefix":"10.1093","volume":"76","author":[{"given":"Ke","family":"Deng","sequence":"first","affiliation":[{"name":"Harvard University , Cambridge , USA"},{"name":"Tsinghua University , Beijing , People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Geng","sequence":"additional","affiliation":[{"name":"Peking University , Beijing , People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun S.","family":"Liu","sequence":"additional","affiliation":[{"name":"Harvard University , Cambridge , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2013,9,18]]},"reference":[{"key":"2023031400061640000_","first-page":"207","volume-title":"Mining association rules between sets of items in large databases","author":"Agrawal","year":"1993"},{"key":"2023031400061640000_","volume-title":"Fast algorithms for mining association rules","author":"Agrawal","year":"1994"},{"key":"2023031400061640000_","first-page":"113","volume-title":"Dynamic topic models","author":"Blei","year":"2006"},{"key":"2023031400061640000_","first-page":"17","article-title":"A correlated topic model of science","volume":"1","author":"Blei","year":"2007","journal-title":"Ann. 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