基于家电产品个性化定制的实例检索方法研究
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摘要
为了研究在大数据条件下的价格权重对相似度计算模型的影响,以及解决传统案例属性之间的相似度计算模型不完全适用于家电产品的个性化定制的问题。文章以相似度的计算模型为研究对象,引入了大数据和统计学的思想,提出了改进的主观赋权模型和属性缺失条件下的修正计算模型。结合组合权重以空调为例来计算定制产品在实例库中的相似度并对比传统的相似度计算模型。通过实例的验证,所提出的权重计算方法有效地提高了检索效率,使检索结果更加可靠。
In order to study the influence of the price weight under the condition of big data on the similarity calculation model,and solve the issues of the similarity calculation model between the traditional case attributes which are not fully applicable to the personalization of home appliances.. This paper takes the similarity calculation model as the research object,and proposes an improved subjective weighting model and a modified calculation model under the condition of attribute deletion by introducing the idea of large data and statistics. Combined with the combination, this paper calculates the similarity of the customized product in the example library of air conditioning and compares the traditional similarity calculation model. Through the verification of the example, the proposed weight calculation method can effectively improve the retrieval efficiency and make the retrieval results more reliable.
In order to study the influence of the price weight under the condition of big data on the similarity calculation model,and solve the issues of the similarity calculation model between the traditional case attributes which are not fully applicable to the personalization of home appliances.. This paper takes the similarity calculation model as the research object,and proposes an improved subjective weighting model and a modified calculation model under the condition of attribute deletion by introducing the idea of large data and statistics. Combined with the combination, this paper calculates the similarity of the customized product in the example library of air conditioning and compares the traditional similarity calculation model. Through the verification of the example, the proposed weight calculation method can effectively improve the retrieval efficiency and make the retrieval results more reliable.
引文
[1] 蒋占四,陈立平,罗年猛.最近邻实例检索相似度分析[J] .计算机集成制造系统,2007,13(6):1165-1168.
[2] AAMODTA,PLAXAE.Case-based reasoning:foundational issues,methodological variations,and system approaches[J].AI Communications,1994,7(1) :39-59.
[3] 周凯波,冯珊,李锋.基于案例属性特征的相似度计算模型研究[ J ] .武汉理工大学学报( 信息与管理工程版) ,2003,23(1):24-27.
[4] 张磊磊,郭增茂,张建华.CBR 系统中案例检索优化方案的设计[J].郑州大学学报(理学版),2014,46(3):119-124.
[5] 陈沛,徐雷,刘坤.家电产品个性化定制实例检索方法的研究[J].组合机床与自动化加工技术,2017(2):101-103.
[6] 李国杰.大数据研究的科学价值[J].中国计算机学会通讯,2012(8):8-15.
[7] 俞立平.大数据与大数据经济学[J].中国软科学,2013(7):177-183.
[8] 王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138.
[9] Guyon I,Elisseeff A.An introduction to variable and feature selection[J].The Journal of Machine Learning Research,2003 ,3:1157-1182.
[10] Yan S ,Xu D ,Zhang B,et al.Graph embedding and extensions:A general framework for dimensionality reduction[J] .IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(1):40-51.
[11] Nguyen H S,Nguyen S H.Discretization methods in data mining[J].Rough Sets in Knowledge Discovery,1998,1:451-482.
[12] Parsons L,Haque E,Liu H.Subspace clustering for high dimensional data:A review[J].ACM SIGKDD Explorations Newsletter,2004,6(1):90-105.
[13] 李文莲,夏健明.基于“大数据”的商业模式创新[J].中国工业经济,2013(5):83-95.
[14] 谷淑娟,高学东,孙冉.一种改进的CBR 案例检索相似性度量模型[J].中国管理信息化,2011,14(9):50-55.
[15] WANG Yingming.On fuzzy multi-attribute decision-making models and methods with incomple te preference information [J] .Fuzzy Sets and Systems,2005,151(2):285-301.
[16] 尚欣,殷国富,杨佐卫,等.基于实例的网络化可重构模块设计技术[J].计算机集成制造系统,2010,16 (4) :703-716.
[17] Slonimt Y,Schneider M.Design issues infuzzy case-based reasoning [J] .Fuzzy Sets and Systems,2001,117(2):251-267.
[2] AAMODTA,PLAXAE.Case-based reasoning:foundational issues,methodological variations,and system approaches[J].AI Communications,1994,7(1) :39-59.
[3] 周凯波,冯珊,李锋.基于案例属性特征的相似度计算模型研究[ J ] .武汉理工大学学报( 信息与管理工程版) ,2003,23(1):24-27.
[4] 张磊磊,郭增茂,张建华.CBR 系统中案例检索优化方案的设计[J].郑州大学学报(理学版),2014,46(3):119-124.
[5] 陈沛,徐雷,刘坤.家电产品个性化定制实例检索方法的研究[J].组合机床与自动化加工技术,2017(2):101-103.
[6] 李国杰.大数据研究的科学价值[J].中国计算机学会通讯,2012(8):8-15.
[7] 俞立平.大数据与大数据经济学[J].中国软科学,2013(7):177-183.
[8] 王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138.
[9] Guyon I,Elisseeff A.An introduction to variable and feature selection[J].The Journal of Machine Learning Research,2003 ,3:1157-1182.
[10] Yan S ,Xu D ,Zhang B,et al.Graph embedding and extensions:A general framework for dimensionality reduction[J] .IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(1):40-51.
[11] Nguyen H S,Nguyen S H.Discretization methods in data mining[J].Rough Sets in Knowledge Discovery,1998,1:451-482.
[12] Parsons L,Haque E,Liu H.Subspace clustering for high dimensional data:A review[J].ACM SIGKDD Explorations Newsletter,2004,6(1):90-105.
[13] 李文莲,夏健明.基于“大数据”的商业模式创新[J].中国工业经济,2013(5):83-95.
[14] 谷淑娟,高学东,孙冉.一种改进的CBR 案例检索相似性度量模型[J].中国管理信息化,2011,14(9):50-55.
[15] WANG Yingming.On fuzzy multi-attribute decision-making models and methods with incomple te preference information [J] .Fuzzy Sets and Systems,2005,151(2):285-301.
[16] 尚欣,殷国富,杨佐卫,等.基于实例的网络化可重构模块设计技术[J].计算机集成制造系统,2010,16 (4) :703-716.
[17] Slonimt Y,Schneider M.Design issues infuzzy case-based reasoning [J] .Fuzzy Sets and Systems,2001,117(2):251-267.