链表模式挖掘利用链表数据结构从事务数据集中挖掘频繁的模式

B. Sandip, A. Apurva
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引用次数: 2

摘要

在过去的几十年中,在频繁模式挖掘领域进行了大量的研究。研究人员已经开发了各种算法来生成频繁的模式。我们提出了使用链表(PML)算法的模式挖掘,该算法使用链表生成频繁模式。它同时使用水平和垂直数据布局。为了生成1项集,它使用水平数据布局,对于2项集或更多项集,它使用垂直数据布局。垂直数据布局的重要特征是,它使用事务id (tid)上的交集操作来快速计算频率。它会自动修剪不相关的数据。该算法采用链表数据结构,使得生成频繁模式所需的执行时间更短。它以高效的内存使用运行。它只扫描数据集两次。实验结果与其他算法进行了比较。
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Pattern mining using Linked list (PML) mine the frequent patterns from transaction dataset using Linked list data structure
The Substantial amount of research has been done in the area of frequent pattern mining in the last few decades. Researchers have developed various algorithms to generate frequent patterns. We propose Pattern Mining using Linked list (PML) algorithm that generates frequent patterns using Linked list. It uses both horizontal and vertical data layout. To generate 1-itemsets, it uses horizontal data layout and for 2-itemsets and more, it uses vertical data layout. The important feature of vertical data layout is that it count the frequency fast using intersection operations on transaction ids (tids). It prunes automatically irrelevant data. The algorithm uses Linked list data structure due to which it takes less execution time to generate frequent patterns. It runs with efficient memory usage. It scans the dataset only two times. The experimental results of proposed algorithm have been compared with other algorithms.
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