Mining Interesting Useful Patterns

频繁模式挖掘是一项重要的数据挖掘任务，它发现频繁共现的项目集。许多现有的算法从精确的数据中找到频繁模式（例如，超市交易），其中数据集的内容是精确已知的。然而，存在数据不精确或不确定的现实情况（例如，传感器数据、医疗测试结果），这是由于诸如固有的测量不准确性或采样频率等因素造成的。尽管这些数据具有不确定性，但它们包含了丰富的有用知识。在过去的几年里，我开发了使用概率方法从不确定数据中找到频繁模式的算法，其中假设每个交易中的项目是独立的。然而，这种假设在许多现实生活中可能不成立。因此，我提出了一个研究计划，其目标是建立一个探索性的，高效的，用户友好的，功能强大的挖掘框架-其中包括挖掘有用的模式，用户感兴趣的数据流和/或不确定的数据的系统。具体来说，我计划

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