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Textual Data Mining Applications for Industrial Knowledge Management Solutions

In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and...

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Main Author: Nadeem Ur-Rahman
Format: Default Thesis
Published: 2010
Subjects:
Online Access:https://hdl.handle.net/2134/6373
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author Nadeem Ur-Rahman
author_facet Nadeem Ur-Rahman
author_sort Nadeem Ur-Rahman (7201364)
collection Figshare
description In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and multiple different data formats may be generated to track the progress of different projects either related to developing new products or providing better services to the customers. Knowledge Discovery from different databases requires considerable efforts and energies and data mining techniques serve the purpose through handling structured data formats. If however the data is semi-structured or unstructured the combined efforts of data and text mining technologies may be needed to bring fruitful results. This thesis focuses on issues related to discovery of knowledge from semi-structured or unstructured data formats through the applications of textual data mining techniques to automate the classification of textual information into two different categories or classes which can then be used to help manage the knowledge available in multiple data formats. Applications of different data mining techniques to discover valuable information and knowledge from manufacturing or construction industries have been explored as part of a literature review. The application of text mining techniques to handle semi-structured or unstructured data has been discussed in detail. A novel integration of different data and text mining tools has been proposed in the form of a framework in which knowledge discovery and its refinement processes are performed through the application of Clustering and Apriori Association Rule of Mining algorithms. Finally the hypothesis of acquiring better classification accuracies has been detailed through the application of the methodology on case study data available in the form of Post Project Reviews (PPRs) reports. The process of discovering useful knowledge, its interpretation and utilisation has been automated to classify the textual data into two classes.
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institution Loughborough University
publishDate 2010
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spelling rr-article-95215552010-01-01T00:00:00Z Textual Data Mining Applications for Industrial Knowledge Management Solutions Nadeem Ur-Rahman (7201364) Mechanical engineering not elsewhere classified Knowledge discovery Knowledge management Data mining Text mining Clustering MKTPKS Termset Mining Decision trees K-nearest Neighbouring (KNN) Naïve Bayes Support Vector Machines (SVMs) Post Project Reviews (PPRs) Mechanical Engineering not elsewhere classified In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and multiple different data formats may be generated to track the progress of different projects either related to developing new products or providing better services to the customers. Knowledge Discovery from different databases requires considerable efforts and energies and data mining techniques serve the purpose through handling structured data formats. If however the data is semi-structured or unstructured the combined efforts of data and text mining technologies may be needed to bring fruitful results. This thesis focuses on issues related to discovery of knowledge from semi-structured or unstructured data formats through the applications of textual data mining techniques to automate the classification of textual information into two different categories or classes which can then be used to help manage the knowledge available in multiple data formats. Applications of different data mining techniques to discover valuable information and knowledge from manufacturing or construction industries have been explored as part of a literature review. The application of text mining techniques to handle semi-structured or unstructured data has been discussed in detail. A novel integration of different data and text mining tools has been proposed in the form of a framework in which knowledge discovery and its refinement processes are performed through the application of Clustering and Apriori Association Rule of Mining algorithms. Finally the hypothesis of acquiring better classification accuracies has been detailed through the application of the methodology on case study data available in the form of Post Project Reviews (PPRs) reports. The process of discovering useful knowledge, its interpretation and utilisation has been automated to classify the textual data into two classes. 2010-01-01T00:00:00Z Text Thesis 2134/6373 https://figshare.com/articles/thesis/Textual_Data_Mining_Applications_for_Industrial_Knowledge_Management_Solutions/9521555 CC BY-NC-ND 4.0
spellingShingle Mechanical engineering not elsewhere classified
Knowledge discovery
Knowledge management
Data mining
Text mining
Clustering
MKTPKS Termset Mining
Decision trees
K-nearest Neighbouring (KNN)
Naïve Bayes
Support Vector Machines (SVMs)
Post Project Reviews (PPRs)
Mechanical Engineering not elsewhere classified
Nadeem Ur-Rahman
Textual Data Mining Applications for Industrial Knowledge Management Solutions
title Textual Data Mining Applications for Industrial Knowledge Management Solutions
title_full Textual Data Mining Applications for Industrial Knowledge Management Solutions
title_fullStr Textual Data Mining Applications for Industrial Knowledge Management Solutions
title_full_unstemmed Textual Data Mining Applications for Industrial Knowledge Management Solutions
title_short Textual Data Mining Applications for Industrial Knowledge Management Solutions
title_sort textual data mining applications for industrial knowledge management solutions
topic Mechanical engineering not elsewhere classified
Knowledge discovery
Knowledge management
Data mining
Text mining
Clustering
MKTPKS Termset Mining
Decision trees
K-nearest Neighbouring (KNN)
Naïve Bayes
Support Vector Machines (SVMs)
Post Project Reviews (PPRs)
Mechanical Engineering not elsewhere classified
url https://hdl.handle.net/2134/6373