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

挖掘序列模式和结构化模式（例如，树、格和图）是重要的数据挖掘任务，具有广泛的应用，如分析客户购买序列和Web页面结构，理解疾病治疗、科学实验、运输和生产过程，检测异常和不寻常模式，发现生物分子序列和结构等。本项目系统地研究了数据挖掘的原理、算法，（2）高度灵活的挖掘方法研究，包括多维多层次序列模式和结构模式的挖掘以及基于约束的挖掘;（3）扩展范围以涵盖基于序列或结构化模式的聚类;（4）多维、多级序列或结构化模式挖掘在入侵检测、Web挖掘和其他重要应用中的应用。 这将为科学和工业应用提供一组高效，可扩展，灵活的顺序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.