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首页> 外文会议>International Symposium on Intelligence Computation and Applications(ISICA 2007); 20070921-23; Wuhan(CN) >The Hybridized Optimization with Gene Expression Programming and Niche Technology for Association Rule Mining
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The Hybridized Optimization with Gene Expression Programming and Niche Technology for Association Rule Mining

机译：基因表达编程与小生境技术的混合优化在关联规则挖掘中的应用

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摘要

Gene Expression Programming (GEP) has found its wide application in the problem of data mining, cellular automata rules for the density-classification and so on. However, unfortunately, the research has shown that the standard GEP, performing worse especially in multi-level spatial search, has certain weaknesses, such as, slow convergence and low accuracy of solutions. Corresponding to this question, a novel Niche GEP (NGEP) is firstly presented in this paper. The algorithm is proposed to establish multi-population and storage structure among the initial chromosomes; the decoded chromosomes and fitness are modified and implemented. And then we apply NGEP to the field of mining association rules. The experimental results show that our algorithm performs better than the alternative evolutionary algorithm in terms of diversity of population and precision; besides, it can discover more association rules that cannot be extracted by other similar method.
机译：基因表达编程（GEP）已在数据挖掘，用于密度分类的细胞自动机规则等问题中得到广泛应用。但是，不幸的是，研究表明，标准GEP表现较差，尤其是在多级空间搜索中，具有某些缺点，例如收敛速度慢和求解精度低。对应于这个问题，本文首先提出了一种新颖的Niche GEP（NGEP）。提出了在初始染色体之间建立多种群和多种群存储结构的算法。修改并实现解码后的染色体和适应度。然后我们将NGEP应用于挖掘关联规则领域。实验结果表明，该算法在种群多样性和精度上均优于替代进化算法。此外，它还可以发现更多其他规则无法提取的关联规则。

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