基于因子分析和K-means聚类算法的行业聚类研究
Research on Industry Clustering Based on Factor Analysis and K-Means Clustering Algorithm

DOI: 10.12677/CSA.2020.1012260, PP. 2447-2456

Keywords: 企业经营范围，行业聚类，因子分析，K-means聚类
Business Scope, Industry Clustering, Factor Analysis, K-Means Clustering

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Abstract:

工商登记信息中的企业经营范围记录了企业主要从事的生产经营活动，是反映企业所属行业类别的重要标准。对企业进行行业聚类，不仅方便国家管理企业，且有利于企业自身定位，顺应国家趋势发展经济。本文采用基于因子分析和K-means聚类算法，以国家发布的《国民经济行业分类》为标准文本，对企业经营字段样本进行行业聚类分析。首先通过因子分析算法得到K-means聚类的最佳聚类个数，然后通过K-means算法，对企业经营范围进行聚类分析，得到每个企业的所属行业类别，最终通过人工评价和戴维森堡丁指数(DBI)评价聚类结果，证明方法的有效性。
The business scope of the enterprise in the industrial and commercial registration information records the main production and operation activities of the enterprise, which is an important standard to reflect the industry category of the enterprise. Industry clustering is not only convenient for the state to manage enterprises, but also conducive to the positioning of enterprises and the development of economy in line with the national trend. In this paper, based on factor analysis and K-means clustering algorithm, and taking the national economic industry classification as the standard text, this paper conducts industry cluster analysis on enterprise business field samples. Firstly, the optimal number of K-means clustering is obtained by factor analysis algorithm, and then the business scope of enterprises is clustered by K-means algorithm, and the industry category of each enterprise is obtained. Finally, the clustering results are evaluated by artificial evaluation and Davies Bouldin index (DBI) to prove the effectiveness of the method.

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