基于数据降维的复杂属性大数据分类方法研究

Research on Big Data Classification of Complex Attributes Basedon Data Dimension Reduction

针对数据的多维度、非线性、不稳定性等特有的复杂属性,在传统数据分类方法的基础上,本研究将高维大数据进行降维处理,结合SVM分类方法和多元SVM-REF分类方法,提出一种基于数据降维的复杂属性大数据分类方法。试验证明,与传统数据分类方法相比,本设计方法能够有效地提升复杂属性大数据的分类效率,也为后续对高维数据分类和数据复杂波动规律分析的深入研究提供依据。

Aiming at the unique complex attributes of data such as multi-dimensionality, non-linearity, and instabili ty, based on the traditional data classification method, this research reduced the dimension of high-dimensional big dat, combined SVM classification method and multivariate SVM-REF classification method, and proposed a method for complex attribute big data classification based on data dimensionality reduction. Experiments show that compared with traditional data classification methods, this design method can effectively improve the classification efficiency of big data with complex attributes, and also provide a basis for subsequent in-depth research on high-dimensional da ta classification and analysis of data complex fluctuation rules.