Mining Interesting Useful Patterns

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Frequent pattern mining is an important data mining task that finds sets of frequently co-occurring items. Many existing algorithms find frequent patterns from precise data (e.g., supermarket transactions), in which the contents of datasets are precisely known. However, there are real-life situations in which data are imprecise or uncertain (e.g., sensor data, medical test results) due to factors like inherited measurement inaccuracies or sampling frequency. Despite their uncertainty, these data contain a rich set of useful knowledge. Over the past few years, I have developed algorithms that use probabilistic approaches to find frequent patterns from uncertain data, in which items in each transaction are assumed to be independent. However, this assumption may not hold in many real-life situations. Hence, I propose a research program with an objective to build an exploratory, efficient, user-friendly, and powerful mining framework--which consists of systems that mine useful patterns that are interesting to users from data streams and/or uncertain data. Specifically, I plan to