[1]张文斌,明 勇,褚维伟,等.基于聚类算法的购物篮压缩研究[J].计算机技术与发展,2018,28(01):169-173.[doi:10.3969/ j. issn.1673-629X.2018.01.036]
 ZHANG Wen-bin,MING Yong,CHU Wei-wei,et al.Research on Shopping Basket Compression Based on Clustering Algorithm[J].Computer Technology and Development,2018,28(01):169-173.[doi:10.3969/ j. issn.1673-629X.2018.01.036]
点击复制

基于聚类算法的购物篮压缩研究
()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年01期
页码:
169-173
栏目:
应用开发研究
出版日期:
2018-01-10

文章信息/Info

Title:
Research on Shopping Basket Compression Based on Clustering Algorithm
文章编号:
1673-629X(2018)01-0169-05
作者:
张文斌明 勇褚维伟黄哲学
深圳大学,广东 深圳 518000
Author(s):
ZHANG Wen-binMING YongCHU Wei-weiHUANG Zhe-xue
Shenzhen University,Shenzhen 518000,China
关键词:
数据挖掘关联规则购物篮压缩购物篮聚类
Keywords:
data miningassociation rulesbasket compressingshopping basket cluster
分类号:
K921/927;TP393
DOI:
10.3969/ j. issn.1673-629X.2018.01.036
文献标志码:
A
摘要:
购物篮分析是数据挖掘技术在零售业的典型应用之一,旨在从零售交易记录中分析出顾客经常同时购买的商品组合,挖掘出购物篮中有价值的信息。 然而实际分析中往往得到的是数以千计的购物篮,企业很难从这数量众多的购物篮中找到真正感兴趣和有价值的,这给实际的应用造成了很大障碍。 针对传统挖掘方法得到购物篮数量过多的问题,定义了一系列特征属性表示购物篮,提出了一种基于 K -Means 层次聚类算法根据属性值对购物篮进行压缩的方法。 该方法通过对真实购物篮进行实验研究与分析。 为验证提出方法的有效性和可行性,将其与传统压缩方法进行了对比。 实验结果表明,相对于其他传统压缩方法,由提出的压缩方法筛选得到的购物篮具有更高的有效性和实用价值,并达到了压缩购物篮集合的效果。
Abstract:
The analysis of shopping basket is one of the typical applications for data mining technology in the retail industry,which aims to analyze the combination of goods which customers frequently buy from the retail transaction records and dig out the valuable information in the shopping basket. However,the thousands of baskets often exist in actual analysis. It is difficult for enterprises from this large number of shopping basket to find that with real interest and value,which brings a great obstacle to application. In view of problem of excessive baskets in traditional mining methods,a series of characteristic attribute are defined for representation of shopping baskets,and a method of K -Means-based hierarchical clustering algorithm compressing the shopping basket according to the attribute value is presented. In order to verify its effectiveness and feasibility,the comparison is made between the proposed and traditional method. The experiment shows that after comparison,the shopping baskets from proposed method own the higher availability and application value with the effect of compressing the shopping basket sets.

相似文献/References:

[1]项响琴 汪彩梅.基于聚类高维空间算法的离群数据挖掘技术研究[J].计算机技术与发展,2010,(01):120.
 XIANG Xiang-qin,WANG Cai-mei.Study of Outlier Data Mining Based on CLIQUE Algorithm[J].Computer Technology and Development,2010,(01):120.
[2]吉同路 柏永飞 王立松.住宅与房地产电子政务中数据挖掘的应用研究[J].计算机技术与发展,2010,(01):235.
 JI Tong-lu,BAI Yong-fei,WANG Li-song.Study and Application of Data Mining in E-government of House and Real Estate Industry[J].Computer Technology and Development,2010,(01):235.
[3]杨静 张楠男 李建 刘延明 梁美红.决策树算法的研究与应用[J].计算机技术与发展,2010,(02):114.
 YANG Jing,ZHANG Nan-nan,LI Jian,et al.Research and Application of Decision Tree Algorithm[J].Computer Technology and Development,2010,(01):114.
[4]赵裕啸 倪志伟 王园园 伍章俊.SQL Server 2005数据挖掘技术在证券客户忠诚度的应用[J].计算机技术与发展,2010,(02):229.
 ZHAO Yu-xiao,NI Zhi-wei,WANG Yuan-yuan,et al.Application of Data Mining Technology of SQL Server 2005 in Customer Loyalty Model in Securities Industry[J].Computer Technology and Development,2010,(01):229.
[5]张笑达 徐立臻.一种改进的基于矩阵的频繁项集挖掘算法[J].计算机技术与发展,2010,(04):93.
 ZHANG Xiao-da,XU Li-zhen.An Advanced Frequent Itemsets Mining Algorithm Based on Matrix[J].Computer Technology and Development,2010,(01):93.
[6]吴楠 胡学钢.基于聚类分区的序列模式挖掘算法研究[J].计算机技术与发展,2010,(06):109.
 WU Nan,HU Xue-gang.Research on Clustering Partition-Based Approach of Sequential Pattern Mining[J].Computer Technology and Development,2010,(01):109.
[7]吴青 傅秀芬.水平分布数据库的正负关联规则挖掘[J].计算机技术与发展,2010,(06):113.
 WU Qing,FU Xiu-fen.Positive and Negative Association Rules Mining on Horizontally Partitioned Database[J].Computer Technology and Development,2010,(01):113.
[8]孙名松 邸明星 王湛昱.多决策树算法在P2P网络流量检测中的应用[J].计算机技术与发展,2010,(06):126.
 SUN Ming-song,DI Ming-xing,WANG Zhan-yu.Application of Decision Tree Algorithm in Traffic Detection of P2P Network[J].Computer Technology and Development,2010,(01):126.
[9]孟魁杰 董莹 赵宗涛.一种基于数据挖掘的无人飞行器故障分析方法[J].计算机技术与发展,2010,(06):225.
 MENG Kui-jie,DONG Ying,ZHAO Zong-tao.A Fault Analysis Method Based on Data Mining for Unmanned Aerial Vehicle[J].Computer Technology and Development,2010,(01):225.
[10]陈伟.Apriori算法的优化方法[J].计算机技术与发展,2009,(06):80.
 CHEN Wei.Method of Apriori Algorithm Optimization[J].Computer Technology and Development,2009,(01):80.
[11]李雷 丁亚丽 罗红旗.基于规则约束制导的入侵检测研究[J].计算机技术与发展,2010,(03):143.
 LI Lei,DING Ya-li,LUO Hong-qi.Intrusion Detection Technology Research Based on Homing - Constraint Rule[J].Computer Technology and Development,2010,(01):143.
[12]王爱平 王占凤 陶嗣干 燕飞飞.数据挖掘中常用关联规则挖掘算法[J].计算机技术与发展,2010,(04):105.
 WANG Ai-ping,WANG Zhan-feng,TAO Si-gan,et al.Common Algorithms of Association Rules Mining in Data Mining[J].Computer Technology and Development,2010,(01):105.
[13]张广路 雷景生 吴兴惠.一种改进的Apriori关联规则挖掘算法(英文)[J].计算机技术与发展,2010,(06):84.
 ZHANG Guang-lu,LEI Jing-sheng,WU Xing-hui.An Improved Apriori Algorithm for Mining Association Rules[J].Computer Technology and Development,2010,(01):84.
[14]文拯 梁建武 陈英.关联规则算法的研究[J].计算机技术与发展,2009,(05):56.
 WEN Zheng,LIANG Jian-wu,CHEN Ying.Research of Association Rules Algorithm[J].Computer Technology and Development,2009,(01):56.
[15]王晓宇 秦锋 程泽凯 邹洪侠.关联规则挖掘技术的研究与应用[J].计算机技术与发展,2009,(05):220.
 WANG Xiao-yu,QIN Feng,CHENG Ze-kai,et al.Investigation and Application of Association Rules Mining[J].Computer Technology and Development,2009,(01):220.
[16]王敏 刘希玉.Apriori算法在税务系统中的应用[J].计算机技术与发展,2009,(11):175.
 WANG Min,LIU Xi-yu.Application of Apriori Algorithm in Tax System[J].Computer Technology and Development,2009,(01):175.
[17]董彩云 刘培华.数据挖掘技术在远程教育教学中的应用[J].计算机技术与发展,2009,(02):179.
 DONG Cai-yun,LIU Pei-hua.Application of Data Mining Technology in Instance Education[J].Computer Technology and Development,2009,(01):179.
[18]刘军锋 李景文 陈大克 邓晓斌.一种改进的关联规则自顶向下算法[J].计算机技术与发展,2008,(02):136.
 LIU Jun-feng,LI Jing-wen,CHEN Da-ke,et al.An Improved Top to Bottom Algorithm for Mining Association Rules[J].Computer Technology and Development,2008,(01):136.
[19]王伟 高亮 吴涛.基于遗传算法的长频繁项集挖掘方法[J].计算机技术与发展,2008,(04):19.
 WANG Wei,GAO Liang,WU Tao.A Method of Mining Long Frequent Itemset Based on Genetic Algorithm[J].Computer Technology and Development,2008,(01):19.
[20]耿波 仲红 徐杰 闫娜娜.用关联分析法对负荷预测结果进行二次处理[J].计算机技术与发展,2008,(04):171.
 GENG Bo,ZHONG Hong,XU Jie,et al.Using Correlation Analysis to Treat Load Forecasting Results[J].Computer Technology and Development,2008,(01):171.

更新日期/Last Update: 2018-03-13