大数据数据挖掘中数据聚类算法的建模与仿真

IF 3.4 Q2 MANAGEMENT
Weiru Chen, J. Oliverio, J. Kim, Jiayue Shen
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引用次数: 13

摘要

大数据是当今流行的前沿技术。技术和算法在工程、生物医学和商业等不同领域不断扩展。由于大数据的大体量和复杂性,在进行数据挖掘时需要进行数据预处理方法。预处理方法包括数据清洗、数据集成、数据约简和数据转换。数据聚类是数据约简中最重要的一步。使用数据聚类,对简化后的数据集进行挖掘应该更有效,同时产生高质量的分析结果。本文介绍了大数据数据挖掘中不同的数据聚类方法和相关算法。数据聚类可以提高数据挖掘的效率和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Modeling and Simulation of Data Clustering Algorithms in Data Mining with Big Data
Big Data is a popular cutting-edge technology nowadays. Techniques and algorithms are expanding in different areas including engineering, biomedical, and business. Due to the high-volume and complexity of Big Data, it is necessary to conduct data pre-processing methods when data mining. The pre-processing methods include data cleaning, data integration, data reduction, and data transformation. Data clustering is the most important step of data reduction. With data clustering, mining on the reduced data set should be more efficient yet produce quality analytical results. This paper presents the different data clustering methods and related algorithms for data mining with Big Data. Data clustering can increase the efficiency and accuracy of data mining.
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来源期刊
CiteScore
17.00
自引率
16.70%
发文量
31
期刊介绍: The Journal of Industrial Integration and Management: Innovation & Entrepreneurship concentrates on the technological innovation and entrepreneurship within the ongoing transition toward industrial integration and informatization. This journal strives to offer insights into challenges, issues, and solutions associated with industrial integration and informatization, providing an interdisciplinary platform for researchers, practitioners, and policymakers to engage in discussions from the perspectives of innovation and entrepreneurship. Welcoming contributions, The Journal of Industrial Integration and Management: Innovation & Entrepreneurship seeks papers addressing innovation and entrepreneurship in the context of industrial integration and informatization. The journal embraces empirical research, case study methods, and techniques derived from mathematical sciences, computer science, manufacturing engineering, and industrial integration-centric engineering management.
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