A data mining tool for learning from manufacturing systems

doi:10.1016/S0360-8352(97)00033-8

Computers & Industrial Engineering

Volume 33, Issues 1–2, October 1997, Pages 27-30

https://doi.org/10.1016/S0360-8352(97)00033-8 Get rights and content

Abstract

This paper describes a software tool, DBMine, developed to assist industrial engineers in data mining. This tool implements three common data mining methodologies: Bacon's algorithm, Decision Trees and DB-Learn. Implemented in Microsoft Visual Basic 3.0©, DBMine, can utilize data in Microsoft Access 2.0© and in Watcom SQL© databases. This paper will also present an example session in which job shop sequences produced by a Genetic Algorithm are explored for regularity.

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There are more references available in the full text version of this article.

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Current applications have been applied in areas where large amounts of data exist, such as quality control, scheduling and control processes of a manufacturing system. Grabot, Blanc, and Binda (1996) and Koonce, Fang, and Tsai (1997) both applied data mining to scheduling problems to modify schedules using fuzzy logic and finding patterns in job shop scheduling through a GA. Lee and Park (2003) presented an agent-based customer centric electronic commerce model in a make-to-order semiconductor manufacturing environment.
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In this work, a mixed integer linear programming (MILP) model is proposed for the multi-class data classification problem using a hyper-box representation. The latter representation is particularly suitable for capturing disjoint data regions. The objective function used is the minimisation of the total number of misclassified data samples. In order to improve the training and testing accuracy of our approach, an iterative solution procedure is developed to assign potential multiple boxes to each single class. Finally, the applicability of the proposed approach is demonstrated through a number of illustrative examples. According to the computational results obtained, the proposed optimisation-based approach is competitive in terms of prediction accuracy when compared with various standard classifiers.
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The objective of this paper is to inform the information systems (IS) manager and business analyst about the role of machine learning techniques in business data mining. Data mining is a fast growing application area in business. Machine learning techniques are used for data analysis and pattern discovery and thus can play a key role in the development of data mining applications. Understanding the strengths and weaknesses of these techniques in the context of business is useful in selecting an appropriate method for a specific application. The paper, therefore, provides an overview of machine learning techniques and discusses their strengths and weaknesses in the context of mining business data. A survey of data mining applications in business is provided to investigate the use of learning techniques. Rule induction (RI) was found to be most popular, followed by neural networks (NNs) and case-based reasoning (CBR). Most applications were found in financial areas, where prediction of the future was a dominant task category.
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Information systemA data mining tool for learning from manufacturing systems

Abstract

Attribute-Oriented Induction in Relational Databases

BACON.1 a General Discovery System

Industrial Scheduling

Information system
A data mining tool for learning from manufacturing systems