摘要： 贝叶斯(Bayesian)方法是近年来数据挖掘中引人注目的研究热点之一，它有效地处理不完备数据、溢出数据和噪声数据之间的序列相关性。该文在对传统序列模式挖掘算法和贝叶斯知识研究的基础上，描述了序列的概率论模型，结合贝叶斯学习，简化了序列模式挖掘过程，提出了一种面向噪声数据的基于贝叶斯方法的序列模式挖掘算法。最后对该算法进行了复杂度分析，并验证了算法性能的优越性。

关键词: 数据挖掘, 贝叶斯方法, 序列模式, 阈值

Abstract: In recent years, Bayesian approach becomes an important method in data mining because of its high ability on processing imperfect, overflow and noisy data. This paper applies Bayesian approach for sequential pattern mining and puts forward the statistics model for sequence data, and more. It designs a new mining algorithm for sequence based on Bayesian theory in a noisy environment, validates the correctness and analyzes the complexity of this algorithm.

Key words: Data mining, Bayesian approach, Sequential patterns, Valve

中图分类号: 

• TP311