Data Mining For Industrial Engineering And Management

Data Mining for Industrial Engineering and Management Chih-Chiang Wei* Department of Digit Fashion Design, Toko University, Taiwan The focus areas of the Industrial Engineering and Management journal include production, logistics, quality, operational research, information systems, technology, communication, industrial economics, regional development, management, organizational behavior, human resources, finance, accounting, marketing, education, training, and professional skills [1]. The aim of this journal is to become a reliable source of information for leaders in the field of industrial engineering management journals research, and to feature a rapid review process [2]. The subject discussed in this paper is data mining for industrial engineering and management. Knowledge Discovery in Databases Knowledge Discovery in Databases (KDD) is an iterative process of extracting implicit, previously unknown, and potentially useful knowledge as a production factor from large datasets [3]. It includes data selection, cleaning, integration, transformation, data mining (DM), and reporting. The KDD process consists of steps that are performed before conducting data mining (i.e., selection, pre-processing, and transformation of data), the actual DM, and subsequent steps (i.e., interpretation, and evaluation of results) [4]. DM refers to the specific step of applying one or more statistical, machine-learning, or image-processing algorithms to a particular dataset with the intent toShow MoreRelatedDesire to Study Industrial Engineering in Operations Research and Information Systems852 Words   |  4 Pagesmy knowledge in Industrial Engineering, particularly in the advanced studies based on Operations Research Information Systems. 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