利用最大跨度位图挖掘频繁序列内和序列间模式

Wenzhe Liao, Qian Wang, Luqun Yang, Jiadong Ren, D. Davis, Changzhen Hu
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引用次数: 3

摘要

频繁序列内模式挖掘和序列间模式挖掘都是针对不同应用进行关联规则挖掘的重要方法。然而,大多数算法只关注其中一个,因为同时尝试两者通常效率低下。为了解决这一缺陷,提出了一种基于位图和maxSpan的序列内和序列间频繁模式挖掘算法FIIP-BM。FIIP-BM将每个事务转换成一个位向量,根据用户需求调整最大跨度,通过逻辑运算得到频繁序列。对于候选2模式生成,首先要检查连接项的下标;如果下标不为0,则连接项的位向量将在计算前左移。左对齐规则适用于不同位向量长度的问题。FIIP-BM可以同时挖掘序列内和序列间的模式。实验验证了FIIP-BM算法的计算速度和存储效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining Frequent Intra-Sequence and Inter-Sequence Patterns Using Bitmap with a Maximal Span
Frequent intra-sequence pattern mining and inter-sequence pattern mining are both important ways of association rule mining for different applications. However, most algorithms focus on just one of them, as attempting both is usually inefficient. To address this deficiency, FIIP-BM, a Frequent Intra-sequence and Inter-sequence Pattern mining algorithm using Bitmap with a maxSpan is proposed. FIIP-BM transforms each transaction to a bit vector, adjusts the maximal span according to user's demand and obtains the frequent sequences by logic And-operation. For candidate 2-pattern generation, the subscripts of the joining items should be checked first; the bit vector of the joining item will be left-shifted before calculation if the subscript is not 0. Left alignment rule is used for different bit vector length problems. FIIP-BM can mine both intra-sequence and inter-sequence patterns. Experiments are conducted to demonstrate the computational speed and memory efficiency of the FIIP-BM algorithm.
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