应用聚类算法对不同维度的数据进行分析

Beenu Mago
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引用次数: 0

摘要

在当今数据驱动的大环境下,分析信息以推动决策和解决问题的能力是成功的基础。这需要一个强大、有效、灵活的数据分析,以帮助快速、直观地构建准确的预测版本。数据分析是现代科学研究各个领域中用于分析数据的常用技术,它包括不同的部门。数据分析包括许多技术。聚类被认为是一种用于分析数据的无监督学习技术。随着学科数量的增加,数据量也随之增加。这导致了应用聚类分析的各种工具和算法的发展。每种聚类算法都有自己的优点和局限性,完全取决于可用信息的复杂性。目前的研究是利用聚类技术对数据进行分析的尝试。研究人员使用python语言编写程序,从企业信息管理系统中收集数据。使用Python进行分析,并对集群进行相应的解释。基于不同维度的数据聚类的结果将导致相应的数据知识的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying clustering algorithm to analyze the data from different dimensions
In today's data-driven panorama, the ability to analyze information to drive decision-making and resolve problems is fundamental for success. This requires a robust, effective, flexible, data analytics that assists to build accurate predictive versions quickly and intuitively. Data analysis is a common technique used to analyze data in various fields of modern scientific research, which includes different divisions of Data analytics include many techniques. Clustering is considered as one of the unsupervised learning technique for analyzing the data. With the increase in number of disciplines, the amount of data is also increased. This results in the development of various tools and algorithms for applying cluster analysis. Each of the clustering algorithm has its own advantages and limitations and it completely depends on the complexity of available information. The current research is an attempt to analyze the data using clustering techniques. The researcher use python language to compile a program to collect the data from an enterprise's information management system. Python is used to analyze and clusters are interpreted accordingly. The results of clustering data based on different dimensions will lead to improve knowledge about the data accordingly.
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