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基于闭合模式的高维生物数据分类算法研究 被引量：1

Research of Classification with Closed Patterns Mining in Long Biological Datasets
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摘要 针对基因表达谱数据的特点提出了基于闭合模式的FEALL分类算法.首先对数据进行预处理,剔除表达谱中的无关基因,从而降低FEALL算法的时间复杂度,减少冗余关联规则的产生;然后根据FEALL算法对行集建立行FP-tree,并对每行建立路径枚举树,挖掘出兴趣规则组的上边界,基于上边界建立分类器对样本进行分类预测,无法识别的样本采用权重判断算法进行判断.实验证明FEALL算法具有较高的效率和预测准确率. This paper proposed an algorithm, FEALL, based on closed pattern. We eliminate the irrelevant genes from gene expression dataset before mining of association rules, then according to FEALL we take row enumeration, build row FP-tree and use upper bounder of Interesting Rule Group to establish classifier. The unrecognizable samples are classified by weight-algorithm. FEALL is proved to be correct and efficient by experiments.
作者 李宏 陈松乔 易丽君 周明 李翔
机构地区 中南大学信息科学与工程学院
出处 《小型微型计算机系统》 CSCD 北大核心 2007年第8期1423-1426,共4页 Journal of Chinese Computer Systems
基金 国家自然科学基金项目(60433020)资助
关键词 关联规则 规则组 闭合模式 上边界 association rules rule group closed pattern upper bounder
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献10

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二级参考文献12

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共引文献50

同被引文献13

  • 1王鹏,吴晓晨,王晨,汪卫,施伯乐.CAPE——数据流上的基于频繁模式的分类算法[J].计算机研究与发展,2004,41(10):1677-1683. 被引量：7
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小型微型计算机系统
2007年 第8期

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