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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/9520

Title: Textual data mining for industrial knowledge management and text classification: a business oriented approach
Authors: Ur-Rahman, Nadeem
Harding, Jennifer A.
Keywords: Textual data mining
Text mining
Post project reviews
Issue Date: 2012
Publisher: © Elsevier
Citation: UR-RAHMAN, N. and HARDING, J.A., 2012. Textual data mining for industrial knowledge management and text classification: a business oriented approach. Expert Systems with Applications, 39 (5), pp. 4729 - 4739
Abstract: Textual databases are useful sources of information and knowledge and if these are well utilised then issues related to future project management and product or service quality improvement may be resolved. A large part of corporate information, approximately 80%, is available in textualdata formats. TextClassification techniques are well known for managing on-line sources of digital documents. The identification of key issues discussed within textualdata and their classification into two different classes could help decision makers or knowledge workers to manage their future activities better. This research is relevant for most text based documents and is demonstrated on Post Project Reviews (PPRs) which are valuable source of information and knowledge. The application of textualdatamining techniques for discovering useful knowledge and classifying textualdata into different classes is a relatively new area of research. The research work presented in this paper is focused on the use of hybrid applications of textmining or textualdatamining techniques to classify textualdata into two different classes. The research applies clustering techniques at the first stage and Apriori Association Rule Mining at the second stage. The Apriori Association Rule of Mining is applied to generate Multiple Key Term Phrasal Knowledge Sequences (MKTPKS) which are later used for classification. Additionally, studies were made to improve the classification accuracies of the classifiers i.e. C4.5, K-NN, Naïve Bayes and Support Vector Machines (SVMs). The classification accuracies were measured and the results compared with those of a single term based classification model. The methodology proposed could be used to analyse any free formatted textualdata and in the current research it has been demonstrated on an industrial dataset consisting of Post Project Reviews (PPRs) collected from the construction industry. The data or information available in these reviews is codified in multiple different formats but in the current research scenario only free formatted text documents are examined. Experiments showed that the performance of classifiers improved through adopting the proposed methodology.
Description: This article was published in the journal, Expert Systems with Applications [© Elsevier]. The definitive version is available at: http://dx.doi.org/10.1016/j.eswa.2011.09.124
Version: Accepted for publication
DOI: 10.1016/j.eswa.2011.09.124
URI: https://dspace.lboro.ac.uk/2134/9520
Publisher Link: http://dx.doi.org/10.1016/j.eswa.2011.09.124
ISSN: 0957-4174
Appears in Collections:Published Articles (Mechanical and Manufacturing Engineering)

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