计算机科学 ›› 2017, Vol. 44 ›› Issue (Z6): 491-494.doi: 10.11896/j.issn.1002-137X.2017.6A.109

• 大数据与数据挖掘 • 上一篇    下一篇

数据挖掘算法在葡萄酒信息数据分析系统中的研究

郝艳妮,吴素萍,田维丽   

  1. 宁夏大学信息工程学院 银川750021,宁夏大学信息工程学院 银川750021,宁夏大学信息工程学院 银川750021
  • 出版日期:2017-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受宁夏科技支撑计划项目(2015BY115),宁夏大学研究生创新项目(GIP201625)资助

Research on Data Mining Algorithm in Wine Information Data Analysis System

HAO Yan-ni, WU Su-ping and TIAN Wei-li   

  • Online:2017-12-01 Published:2018-12-01

摘要: 随着信息科技的快速发展,计算机中的经典算法在葡萄酒产业中得到了广泛的研究与应用。机器学习算法的特点是运用人工智能技术,在经过大量的样本集训练和学习后可以自动地找出运算所需要的参数和模型。针对数据挖掘中常用的机器学习算法进行相关的研究。以分类算法为例进行数据挖掘技术的研究。针对SVM(支持向量机)泛化能力弱的缺点,给出了一种改进的SVM-NSVM,即先对训练集进行精选,根据每个样本与最近邻类标的异同判断样本点的取舍,然后再用SVM训练得到分类器。针对kNN(k-最近邻)训练数据集大的缺点,给出了一种改进的通过渐进的思想来寻找最近邻点。实验表明,与SVM相比,NSVM在分类正确率、分类速度上有一定的优势。改进的kNN算法的复杂度明显降低。此外,设计了葡萄酒信息数据分析系统,利用数据挖掘方法对极大量的葡萄酒信息数据进行分析、对比与匹配,从而可挖掘葡萄酒的主要成分对比信息和营销潜在信息等;再对这些成分进行相应的分析,并与高质量葡萄酒中的成分进行相应的对比,最终得出葡萄酒的相关分析信息数据,其可帮助葡萄酒生产厂商对葡萄酒的成分含量、品质进行分析。

关键词: 机器学习,数据挖掘,分类算法,葡萄酒,数据分析

Abstract: With the rapid development of information technology,the classical algorithm in computer has been extensively studied and applied in wine industry.The characteristics of machine learning algorithm are to use the technology of artificial intelligence,and a large number of samples in the set of training and learning can atuomatically identify the model and parameters that operation needs.Related research was used in data mining machine learning algorithms in this paper.The research of data mining technology based on classification algorithm was taken as an example.And for the weak generalization ability of SVM(Support Vector Machine),we proposed an improved SVM-NSVM,in which the training set is selected precisely according to each sample,similarities and differences between the subject nearest class choice is decided,and then SVM is trained to get classifier.For big disadvantage of the training data set in kNN’s(K-Nearest Neighbor),an improved progressive idea was given to find the nearest neighbor.Experiments show that,NSVM has more advantages than SVM in classification accuracy,speed classification.Complexity of the improved kNN algorithm is significantly reduced.In addition,the wine information and data analysis system was designed,and the data mining method was used to analyze,contrast and match the extremely large amount of wine information data so as to excavate the comparative information of the main components of wine and marketing potential information.Then these components was designed accordingly.With high-quality wine in the corresponding comparison of the ingredients,the final analysis analyze wine-related information and data can help wine producers analyze wine content and wine quality.

Key words: Machine learning,Data mining,Classification algorithm,Wine,Data analysis

[1] ESCUDERO A,GOGORZA B,MELUSA M A,et al.Characterization of the aroma of a wine from maccabeo.Key role played by compounds with low odor activity values[J].Journal of Agricultural and Food Chemistry,2004,2(11):3516-3524.
[2] 李华.葡萄酒的原产地域命名与感官评价[C]∥第二届国际葡萄与葡萄酒学术研讨会.2001:29-34
[3] 李华.中国葡萄酒原产地域产品命名系统[J].酿酒科技,2001,4(2):63-68.
[4] NOBLE A C,ARNOLD R A,BUECHSENSTEIN J,et al.Modification of a standardized system of wine aroma terminology[J].American Journal of Enology and Viticulture,1987,8(2):143-146.
[5] DANZART M,SIEFFERMANN J M.Analyse sensorielle etmise en place dun laboratoir[J].Revue Des Oenologues,2001,7(5):31-35.
[6] 李华,刘勇强,梁新红,等.运用多元统计分析确定葡萄酒感官特性的描述符[J].中国食品学报,2007,7(4):114-119.
[7] 齐桂梅.葡萄与葡萄酒中的酚元化合物[J].葡萄栽培与酿酒,1992,1(2):41-42.
[8] FOW LES G W A.Acids in grapes and wines:Areview[J].Journal of wine research,Grnande-Bretagne,1992,2(3):25-411.
[9] 王运照,胡文忠,李婷婷,等.冰葡萄酒酿造过程中糖分与乙醇变化的研究[J].食品工业科技,2015,0(17):142-145.
[10] 李华,王庆伟,刘树文,等.智能系统在葡萄酒产业中应用的研究进展[J].农业工程学报,2006,2(7):193-199.
[11] 李艳芳.基于多Agent系统的Web数据挖掘技术[J].计算机工程与设计,2007,8(6):1267-1272.
[12] 王爱平,徐晓艳,李仿华,等.基于改进KNN算法的中文文本分类方法[J].微型机与应用,2011,0(18):8-13.
[13] 张巍,张功萱,王永利,等.基于CUDA的SVM 算法并行化研究[J].计算机科学,2013,0(4):69-73.
[14] 熊亚军,廖晓农,李梓铭,等.KNN数据挖掘算法在北京地区霾等级预报中的应用[J].气象,2015,1(1):98-104.
[15] 丁世飞,齐丙娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,0(1):2-10.
[16] 黄飞,周军,卢晓东.基于马氏距离的一维距离像识别算法仿真[J].计算机仿真,2010,7(3):31-34.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!