数据挖掘算法在葡萄酒信息数据分析系统中的研究
发布时间：2018-11-16 18:25
【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。
[Abstract]:With the rapid development of information technology, the classical computer algorithms have been widely studied and applied in wine industry. The characteristic of machine learning algorithm is that the parameters and models needed for operation can be found automatically by using artificial intelligence technology after training and learning a large number of sample sets. This paper focuses on the machine learning algorithms commonly used in data mining. The classification algorithm is taken as an example to study the data mining technology. Aiming at the weakness of the generalization ability of SVM (support Vector Machine), an improved SVM-NSVM, is presented to select the training set first and judge the choice of the sample points according to the similarities and differences between each sample and the nearest neighbor. Then the classifier is trained by SVM. In view of the disadvantage of large kNN (k- nearest neighbor) training data set, an improved approach to finding nearest neighbor points by progressive thinking is presented. Experimental results show that NSVM has some advantages in classification accuracy and classification speed compared with SVM. The complexity of the improved kNN algorithm is obviously reduced. In addition, a wine information data analysis system is designed, and a large number of wine information data are analyzed, compared and matched by the method of data mining, so that the main components of wine contrast information and marketing potential information can be mined. Then these components are analyzed and compared with those in high quality wine. Finally, the relevant analysis information data of wine can be obtained, which can help the wine producers to know the composition content of wine. Quality analysis.
【作者单位】： 宁夏大学信息工程学院;
【基金】：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
【分类号】：TP311.13
本文编号：2336285
[Abstract]:With the rapid development of information technology, the classical computer algorithms have been widely studied and applied in wine industry. The characteristic of machine learning algorithm is that the parameters and models needed for operation can be found automatically by using artificial intelligence technology after training and learning a large number of sample sets. This paper focuses on the machine learning algorithms commonly used in data mining. The classification algorithm is taken as an example to study the data mining technology. Aiming at the weakness of the generalization ability of SVM (support Vector Machine), an improved SVM-NSVM, is presented to select the training set first and judge the choice of the sample points according to the similarities and differences between each sample and the nearest neighbor. Then the classifier is trained by SVM. In view of the disadvantage of large kNN (k- nearest neighbor) training data set, an improved approach to finding nearest neighbor points by progressive thinking is presented. Experimental results show that NSVM has some advantages in classification accuracy and classification speed compared with SVM. The complexity of the improved kNN algorithm is obviously reduced. In addition, a wine information data analysis system is designed, and a large number of wine information data are analyzed, compared and matched by the method of data mining, so that the main components of wine contrast information and marketing potential information can be mined. Then these components are analyzed and compared with those in high quality wine. Finally, the relevant analysis information data of wine can be obtained, which can help the wine producers to know the composition content of wine. Quality analysis.
【作者单位】： 宁夏大学信息工程学院;
【基金】：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
【分类号】：TP311.13
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