一种基于情节矩阵和频繁情节树的情节挖掘方法

针对现有的最小发生的频繁情节挖掘中存在的问题,提出一种发现情节的不同最小发生并对其进行计数的方法.在此基础上,提出基于情节矩阵和频繁情节树的最小发生频繁情节挖掘方法,基于直接扩展思想,只需扫描数据一次,不需生成候选情节,提高了挖掘的时空效率.提出了基于相同结点链和哈希链的优化方法,通过省略相同结点的扩展过程,进一步提高了挖掘性能.最后,在不同类型的真实数据集上进行实验,实验结果验证了所提出的频繁情节挖掘方法的优势以及优化方法的有效性和高效性.

An Episode Mining Method Based on Episode Matrix and Frequent Episode Tree

For the problems existing in current frequent episode mining with minimal occurrences, the paper proposes
a method of discovering distinct minimal occurrences and counting them. On this basis, an episode matrix and frequent episode tree based mining method is proposed, which only scans event sequences once based on the idea of direct extension without candidate generation and enhances space-time efficiency. Moreover, its optimization is presented based on same node chains and hash chains, which improves the mining performance further by omitting the extension process of same nodes. A series of experiments on different types of real data sets show the advantage of the proposed method, and validate the effectiveness and efficiency of the optimization method.