一种改进的线性时间封闭项集挖掘算法  

An Improved Linear time Closed Item Sets Mining Algorithm

作　　者：徐学红[1] 陆伟[2] 杨余旺[2] XU Xue-hong;LU Wei;YANG Yu-wang(School of Information and Electronic Engineering,Henan University of Animal Husbandry and Economy Zhengzhou 450044,China;School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]河南牧业经济学院信息与电子工程学院,郑州450044 [2]南京理工大学计算机科学与工程学院,南京210094

出　　处：《科学技术与工程》2018年第18期241-246,共6页Science Technology and Engineering

基　　金：国家自然科学基金(61640020)资助

摘　　要：主流数据挖掘算法不能有效解决大规模数值数据集挖掘问题。提出了一种应用于大规模数值数据集改进的线性时间封闭项集挖掘(improved linear time closed item sets mining,ILCM)算法。ILCM算法使用能够提取属性共同变化量的渐进模式挖掘方法,借鉴LCM算法的前缀保留闭合扩展思想,通过深度优先搜索输出频繁封闭渐进项集结果。实验证明,相比传统挖掘算法,ILCM能够显著提高算法运行效率和降低内存空间占用;并且能够有效处理如DNA微阵列等实际大型数值数据集挖掘。The current data mining algorithms cannot be used to solve the large scale numerical dataset.A novel algorithm called improved linear time closed item sets minner(ILCM)is proposed to be applied for the scalable numerical datasets.Based on ppc(prefix preserving closure)-extension principle of the standard LCM,ILCM uses gradual pattern mining method with the capability of extracting the covariation of attributes,and then outputs the result of closed frequent gradual item sets according to depth first searching method.The experimental demonstrates that,compared to the traditional mining algorithm,ILCM can improve the running efficiency and reduce memory space occupation significantly,and ILCM is proven to apply in the practical large scale numerical dataset such as DNA micro-array data.

关 键 词：渐进模式 频繁封闭项集 渐进模式 共同变化量 运行效率 内存空间占用 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]