Abstract
Over the past decades, many industries have heavily spent on computerizing their work environments with the intention to simplify and expedite access to information and its processing. Typical of real-world data are various types of imperfections, uncertainties, ambiguities, that have complicated attempts at automated knowledge discovery. Indeed, it soon became obvious that adequate methods to deal with these problems were critically needed. Simple methods such as "interpolating" or just ignoring data imperfections being found often to lead to inferences of dubious practical value, the search for appropriate modification of knowledge-induction techniques began. Sometimes, rather non-standard approaches turned out to be necessary. For instance, the probabilistic approaches by earlier works are not sufficiently capable of handling the wider range of data imperfections that appear in many new applications (e.g., medical data). Dempster-Shafer theory provides a much stronger framework, and this is why it has been chosen as the fundamental paradigm exploited in this dissertation. The task of association rule mining is to detect frequently co-occurring groups of items in transactional databases. The majority of the papers in this field concentrate on how to expedite the search. Less attention has been devoted to how to employ the identified frequent itemsets for prediction purposes; worse still, methods to tailor association-mining techniques so that they can handle data imperfections are virtually nonexistent. This dissertation proposes a technique referred to by the acronym DS-ARM (Dempster-Shafer based Association Rule Mining) where the DS-theoretic framework is used to enhance a more traditional association-mining mechanism. Of particular interest is here a method to employ the knowledge of partial contents of a "shopping cart" for the prediction of what else the customer is likely to add to it. This formalized problem has many applications in the analysis of medical databases. A recently-proposed data structure, an itemset tree (IT-tree), is used to extract association rules in a computationally efficient manner, thus addressing the scalability problem that has disqualified more traditional techniques from real-world applications. The proposed algorithm is based on the Dempster-Shafer theory of evidence combination. Extensive experiments explore the algorithm's behavior; some of them use synthetically generated data, others relied on data obtained from a machine-learning repository, yet others use a movie ratings dataset or a HIV/AIDS patient dataset.