JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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Rizwan Iqbal and Masrah Azrifah Azmi Murad Heflin, J., & Hendler, J. (2000). Searching the web with SHOE. Workshop on AI for Web Search (AAAI- 2000). Iqbal, R., Murad, M. A. A., Selamat, M. H., & Azman, A. (2012). Negation query handling engine for natural language interfaces to ontologies. International Conference on Information Retrieval and Knowledge Management (CAMP’12), 249-253. Kaufmann, E., Bernstein, A., & Fischer, L. (2007). Nlp-Reduce: A “naïve” but domain independent natural language interface for querying ontologies. European Semantic Web Conference (ESWC 2007). Kaufmann, E., Bernstein, A., & Zumstein, R. (2006). Querix: A Natural Language Interface to Query Ontologies Based on Clarification Dialogs. International Semantic Web Conference (ISWC 2006), Springer, 980-981. Lei,Y., Uren, V., & Motta, E. (2006). Semsearch: a search engine for the semantic web. Managing Knowledge in a World of Networks, 4248, 238-245. Motro, A. (1986). Constructing queries from tokens. SIGMOD international conference on Management of data (SIGMOD ‘86), 120-131. MooneyData. (1994). Retrieved from http://www.cs.utexas.edu/users/ml/nldata.html. Rocha, C., Schwabe, D., & de Aragao, M. (2004). A Hybrid Approach for Searching in the Semantic Web. 13 th International conference on World Wide Web (WWW’04), 374-383. Ramachandran, V. A., & Krishnamurthi, I. (2009). NLION: Natural Language interface for querying ontologies. 2 nd Bangalore Annual Compute Conference (COMPUTE’ 09), 1-4. Tablan,V., Damljanovic, D., & Bontcheva, K. (2008a). A natural language query interfaceto structured information. 5 th European semantic web conference on The semantic web: research and applications (ESWC’08), Springer Berlin/Heidelberg, LNCS, 5021, 361–375. Tablan, V., Damljanovic, D., & Bontcheva, K. (2008b). A natural language query interface to structured information. European Semantic Web Conference (ESWC 2008), 361-375. Vanessa, L., Victoria, U., Motta, E., & Michele, P. (2007). AquaLog: an ontology-driven question answering system for organizational semantic intranets. Journal of Web Semantics, 5, 72–105. Wang, H., Zhang, K., Liu, Q., Tran, T., & Yu, Y. (2008). Q2semantic: A lightweight keyword interface to semantic search. European Semantic Web Conference (ESWC ‘08), 584–598. Wang, C., Xiong, M., Zhou, Q., & Yu, Y. (2007). PANTO: A Portable Natural Language Interface to Ontologies. European Semantic Web Conference (ESWC ‘07), 473-487. Zhou, Q., Wang, C., Xiong, M., Wang, H., & Yu, Y. (2007). Spark: Adapting keyword query to semantic search. International Semantic Web Conference (ISWC)/Asian Semantic Web Conference (ASWC), 694-707. 204 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
ISSN: 0128-7680 © 2013 Universiti Putra Malaysia Press. Pertanika J. Sci. & Technol. 21 (1): 205 - 216 (2013) SCIENCE & TECHNOLOGY Journal homepage: http://www.pertanika.upm.edu.my/ Modified Multi-Class Classification using Association Rule Mining Yuhanis Yusof* and Mohammed Hayel Refai School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia ABSTRACT As the amount of document increases, automation of classification that aids the analysis and management of documents receive focal attention. Classification, based on association rules that are generated from a collection of documents, is a recent data mining approach that integrates association rule mining and classification. The existing approaches produces either high accuracy with large number of rules or a small number of association rules that generate low accuracy. This work presents an association rule mining that employs a new item production algorithm that generates a small number of rules and produces an acceptable accuracy rate. The proposed method is evaluated on UCI datasets and measured based on prediction accuracy and the number of generated association rules. Comparison is later made against an existing classifier, Multi-class Classification based on Association Rule (MCAR). From the undertaken experiments, it is learned that the proposed method produces similar accuracy rate as MCAR but yet uses lesser number of rules. Keywords: Classification, association rule, rule mining, rule production, data mining INTRODUCTION The availability of high performance computers, data collection tools and huge memory capacities has made gathering and saving huge quantities of data possible. For example, the number of sales transactions executed during one year in a large retail supermarket such as Carrefour is numerous and the amount of data on the World Wide Web (WWW) is extremely massive as well. This vast growth of stored databases provides an opportunity for new automated intelligent data analysis methods that summarizes information from these Article history: Received: 31 March 2012 Accepted: 31 August 2012 E-mail addresses: yuhanis@uum.edu.my (Yuhanis Yusof), mohammdrefai@yahoo.com (Mohammed Hayel Refai) *Corresponding Author databases. The process of generating this useful knowledge is accomplished using data mining techniques. Liu et al. (1998) define data mining as one of the primary steps in Knowledge Discovery from Databases (KDD), which finds and generates useful
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Selected Articles from UPM-Malaysia
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CONCLUSION Heng, S. C., Ibrahim, Z.
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Surface Roughness Y. Yusuf, J. M. J
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DISCUSSION Y. Yusuf, J. M. Juoi, Z.
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Modelling of Motion Resistance Rati
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Statistical Analysis Norhashila Has
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INTRODUCTION Tajau, R. et al. A hig
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N. Maizatul, I. Norazowa, W. M. Z.
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The Role of Similarity Measurement
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REFEREES FOR THE PERTANIKA JOURNAL
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Guidelines for Authors Publication
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table should be prepared on a separ
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Usability of Educational Computer G
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