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You searched for subject:(Na ve Bayes). Showing records 1 – 3 of 3 total matches.

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Loughborough University

1. Ur-Rahman, Nadeem. Textual data mining applications for industrial knowledge management solutions.

Degree: PhD, 2010, Loughborough University

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.

Subjects/Keywords: 020; Knowledge discovery; Knowledge management; Data mining; Text mining; Clustering; MKTPKS Termset Mining; Decision trees; K-nearest Neighbouring (KNN); Na?ve Bayes; Support Vector Machines (SVMs); Post Project Reviews (PPRs)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ur-Rahman, N. (2010). Textual data mining applications for industrial knowledge management solutions. (Doctoral Dissertation). Loughborough University. Retrieved from https://dspace.lboro.ac.uk/2134/6373 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519965

Chicago Manual of Style (16th Edition):

Ur-Rahman, Nadeem. “Textual data mining applications for industrial knowledge management solutions.” 2010. Doctoral Dissertation, Loughborough University. Accessed January 19, 2020. https://dspace.lboro.ac.uk/2134/6373 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519965.

MLA Handbook (7th Edition):

Ur-Rahman, Nadeem. “Textual data mining applications for industrial knowledge management solutions.” 2010. Web. 19 Jan 2020.

Vancouver:

Ur-Rahman N. Textual data mining applications for industrial knowledge management solutions. [Internet] [Doctoral dissertation]. Loughborough University; 2010. [cited 2020 Jan 19]. Available from: https://dspace.lboro.ac.uk/2134/6373 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519965.

Council of Science Editors:

Ur-Rahman N. Textual data mining applications for industrial knowledge management solutions. [Doctoral Dissertation]. Loughborough University; 2010. Available from: https://dspace.lboro.ac.uk/2134/6373 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519965


Loughborough University

2. Ur-Rahman, Nadeem. Textual data mining applications for industrial knowledge management solutions.

Degree: PhD, 2010, Loughborough University

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.

Subjects/Keywords: 020; Knowledge discovery; Knowledge management; Data mining; Text mining; Clustering; MKTPKS Termset Mining; Decision trees; K-nearest Neighbouring (KNN); Na?ve Bayes; Support Vector Machines (SVMs); Post Project Reviews (PPRs)

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ur-Rahman, N. (2010). Textual data mining applications for industrial knowledge management solutions. (Doctoral Dissertation). Loughborough University. Retrieved from http://hdl.handle.net/2134/6373

Chicago Manual of Style (16th Edition):

Ur-Rahman, Nadeem. “Textual data mining applications for industrial knowledge management solutions.” 2010. Doctoral Dissertation, Loughborough University. Accessed January 19, 2020. http://hdl.handle.net/2134/6373.

MLA Handbook (7th Edition):

Ur-Rahman, Nadeem. “Textual data mining applications for industrial knowledge management solutions.” 2010. Web. 19 Jan 2020.

Vancouver:

Ur-Rahman N. Textual data mining applications for industrial knowledge management solutions. [Internet] [Doctoral dissertation]. Loughborough University; 2010. [cited 2020 Jan 19]. Available from: http://hdl.handle.net/2134/6373.

Council of Science Editors:

Ur-Rahman N. Textual data mining applications for industrial knowledge management solutions. [Doctoral Dissertation]. Loughborough University; 2010. Available from: http://hdl.handle.net/2134/6373


NSYSU

3. Cheng, Tsang-Hsiang. A Clustering-based Approach to Document-Category Integration.

Degree: PhD, Information Management, 2003, NSYSU

E-commerce applications generate and consume tremendous amount of online information that is typically available as textual documents. Observations of textual document management practices by organizations or individuals suggest the popularity of using categories (or category hierarchies) to organize, archive and access documents. On the other hand, an organization (or individual) also constantly acquires new documents from various Internet sources. Consequently, integration of relevant categorized documents into existent categories of the organization (or individual) becomes an important issue in the e-commerce era. Existing categorization-based approach for document-category integration (specifically, the Enhanced Naïve Bayes classifier) incurs several limitations, including homogeneous assumption on categorization schemes used by master and source catalogs and requirement for a large-sized master categories as training data. In this study, we developed a Clustering-based Category Integration (CCI) technique to deal with integrating two document catalogs each of which is organized non-hierarchically (i.e., in a flat set). Using the Enhanced Naïve Bayes classifier as benchmarks, the empirical evaluation results showed that the proposed CCI technique appeared to improve the effectiveness of document-category integration accuracy in different integration scenarios and seemed to be less sensitive to the size of master categories than the categorization-based approach. Furthermore, to integrate the document categories that are organized hierarchically, we proposed a Clustering-based category-Hierarchy Integration (referred to as CHI) technique extended the CCI technique and for category-hierarchy integration. The empirical evaluation results showed that the CHI technique appeared to improve the effectiveness of hierarchical document-category integration than that attained by CCI under homogeneous and comparable scenarios. Advisors/Committee Members: San-Yi Huang (chair), Chih-Ping Wei (committee member), Lee-Feng Chien (chair), Hsing Cheng (chair), Fu-Ren Lin (chair), Hsin-Hui Lin (chair), Shin-Mu Tseng (chair).

Subjects/Keywords: Catalog Integration; Document Category Integration; Naï; ve Bayes Classifier; Hierarchical Category Integration; Hierarchical Clustering; Document Clustering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cheng, T. (2003). A Clustering-based Approach to Document-Category Integration. (Doctoral Dissertation). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0904103-201802

Chicago Manual of Style (16th Edition):

Cheng, Tsang-Hsiang. “A Clustering-based Approach to Document-Category Integration.” 2003. Doctoral Dissertation, NSYSU. Accessed January 19, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0904103-201802.

MLA Handbook (7th Edition):

Cheng, Tsang-Hsiang. “A Clustering-based Approach to Document-Category Integration.” 2003. Web. 19 Jan 2020.

Vancouver:

Cheng T. A Clustering-based Approach to Document-Category Integration. [Internet] [Doctoral dissertation]. NSYSU; 2003. [cited 2020 Jan 19]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0904103-201802.

Council of Science Editors:

Cheng T. A Clustering-based Approach to Document-Category Integration. [Doctoral Dissertation]. NSYSU; 2003. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0904103-201802

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