{"title":"The Modeling and Simulation of Data Clustering Algorithms in Data Mining with Big Data","authors":"Weiru Chen, J. Oliverio, J. Kim, Jiayue Shen","doi":"10.1142/S2424862218500173","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":51835,"journal":{"name":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","volume":"22 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2018-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Industrial Integration and Management-Innovation and Entrepreneurship","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S2424862218500173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 13
Abstract
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.
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.