Mining Sequential Patterns and Structured Patterns: Scalability, Flexibility, Extensibility, and Applicability

序列模式和结构化模式(如树、格和图)的挖掘是一项重要的数据挖掘任务，具有广泛的应用前景，如客户购买序列和网页结构的分析、疾病治疗的理解、科学实验、运输和生产过程、异常和异常模式的检测、生物分子序列和结构的发现等。本课题对可伸缩序列和结构化模式挖掘的原理、算法和应用进行了系统的研究，主要内容包括：(1)高可伸缩性挖掘算法的开发，包括挖掘最大模式、闭合模式和top-k模式；(2)研究高度灵活的挖掘方法，包括多维多层序列和结构化模式的挖掘和基于约束的挖掘；(3)将范围扩展到基于序列或结构化模式的聚类；以及(4)多维、多层序列或结构化模式挖掘在入侵检测、Web挖掘等重要应用中的应用。这将为科学和工业应用带来一套高效、可扩展和灵活的序列和结构化模式挖掘方法。

Mining sequential patterns and structured patterns (e.g., trees, lattices, and graphs) are important data mining tasks, with broad applications, such as the analysis of customer purchase sequences and Web page structures, understanding of disease treatments, scientific experiments, transportation and production processes, detection of anomaly and unusual patterns, discovery of bio-molecule sequences and structures, and so on.This project is to perform a systematic investigation of the principles, algorithms, and applications of scalable sequential and structured pattern mining, which covers the following issues: (1) development of highly scalable mining algorithms, including mining max-patterns, closed patterns and top-k patterns; (2) investigation of highly flexible mining methodologies, including mining of multi-dimensional multi-level sequential and structured patterns and constraint-based mining; (3) extension of the scope to cover sequential or structured pattern-based clustering; and (4) application of multi-dimensional, multi-level sequential or structured pattern mining for intrusion detection, Web mining, and other important applications. This will lead to a set of efficient, scalable, and flexible sequential and structured pattern mining methods for scientific and industrial applications.