基于ssep - tree的强跳跃新模式挖掘改进算法

Xiangtao Chen, Lijuan Lu
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引用次数: 5

摘要

跳跃新兴模式(JEPs)是一种数据挖掘模型,用于发现一组分类事务数据集之间存在的差异。然而,当前的jep挖掘算法通常是耗时的,并且在最小支持下进行修剪可能需要进行多次调整。本文研究了强跳跃新兴模式(Strong Jumping Emerging Patterns, SJEPs),这是一种被认为是最具差异化的高质量模式。我们提出了一种改进的基于树的方法来有效地挖掘两类数据的ssep。实验结果表明,该算法是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved algorithm of mining Strong Jumping Emerging Patterns based on sorted SJEP-Tree
Jumping Emerging Patterns (JEPs) are a data mining model that is useful as a mean of discovering differences present amongst a collection of classified transaction datasets. However, current JEPs mining algorithms are usually time-consuming and pruning with minimum support may require several adjustments. In this paper, we investigate Strong Jumping Emerging Patterns (SJEPs), which are believed to be high quality patterns with the most differentiating power. We propose an improved tree-based method to effectively mine SJEPs of two data classes. Experimental results show that our algorithm is effective.
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