Mining Interesting Useful Patterns

频繁模式挖掘是发现频繁共现项集的重要数据挖掘任务。许多现有的算法从精确的数据(如超市交易)中发现频繁模式，其中数据集的内容是精确已知的。然而，在现实生活中，由于遗传测量不准确或采样频率等因素，数据不精确或不确定(例如，传感器数据、医学测试结果)。尽管存在不确定性，但这些数据包含了丰富的有用知识。在过去的几年里，我开发了一些算法，使用概率方法从不确定的数据中找到频繁模式，其中每个交易中的项目都被假设为独立的。然而，这一假设在许多现实生活中可能并不成立。因此，我提出了一个研究计划，目标是构建一个探索性的、高效的、用户友好的和强大的挖掘框架--它由从数据流和/或不确定数据中挖掘用户感兴趣的有用模式的系统组成。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