On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study
Joint Authors
Pacella, Massimo
Grieco, Antonio
Blaco, Marzia
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In modern industry, the development of complex products involves engineering changes that frequently require redesigning or altering the products or their components.
In an engineering change process, engineering change requests (ECRs) are documents (forms) with parts written in natural language describing a suggested enhancement or a problem with a product or a component.
ECRs initiate the change process and promote discussions within an organization to help to determine the impact of a change and the best possible solution.
Although ECRs can contain important details, that is, recurring problems or examples of good practice repeated across a number of projects, they are often stored but not consulted, missing important opportunities to learn from previous projects.
This paper explores the use of Self-Organizing Map (SOM) to the problem of unsupervised clustering of ECR texts.
A case study is presented in which ECRs collected during the engineering change process of a railways industry are analyzed.
The results show that SOM text clustering has a good potential to improve overall knowledge reuse and exploitation.
American Psychological Association (APA)
Pacella, Massimo& Grieco, Antonio& Blaco, Marzia. 2016. On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099695
Modern Language Association (MLA)
Pacella, Massimo…[et al.]. On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099695
American Medical Association (AMA)
Pacella, Massimo& Grieco, Antonio& Blaco, Marzia. On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099695
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099695