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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Intelligent XML Query-Answering Support with Efficiently Updating XML Data in Data Mining

Parag Zaware, Prabhudev.I, , ,
Vishwabharati Academy College of Engineering, Ahmednagar

Data is present in various unstructured format. Extracting information from non structured documents is a very difficult task and it is become more and more critical when the amount of digital information available over the internet increases. This paper is based on design of Branch Organization Rule (BOR) results in approximate answer of queries for mining. XML is popular portable language best suitable for many web technologies hence we prefer XML. While implementing XML Query Answering we are going to implement Naïve Bayes as Machine learning algorithm which we will use specially for Query Classification. We are also implementing same concept for rules classifications by using which the trees are generated after applying queries. Due to creating classification of queries our accuracy of results will increase.

Parag Zaware,Prabhudev.I," Intelligent XML Query-Answering Support with Efficiently Updating XML Data in Data Mining”, International Journal Of Computer Engineering In Research Trends, 3(12):613-619,December-2016.

Keywords : XML, Mining, query answer, Machine Learning.

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[2]A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke, 
“Privacy Preserving Mining of Association Rules,” Proc. Eighth ACM Int’l Conf. Knowledge Discovery and Data Mining, pp. 217-228, 2002.
[3]C. Combi, B. Oliboni, and R. Rossato, “Querying XML 

Documents by Using Association Rules,” Proc. 16th Int’l Conf. Database and Expert Systems Applications, pp. 1020-1024, 2005

[4]D. Braga, A. Campi, S. Ceri, M. Klemettinen, and P. Lanzi, “Discovering Interesting Information in XML Data with Asso-ciation Rules,” Proc. ACM Symp. Applied Computing, pp. 450-454, 2003. 

[5]D. Barbosa, L. Mignet, and P. Veltri, “Studying the XML 
Web: Gathering Statistics from an XML Sample,” World Wide Web, vol. 8, no. 4, pp. 413-438, 2005.

[6]E. Baralis, P. Garza, E. Quintarelli, and L. Tanca, “Answer-ingXMLQueries	by Means of Data Summaries,” ACM Trans.InformationSystems, vol. 25, no. 3, p. 10, 2007.

[7]L. Feng, T.S. Dillon, H. Weigand, and E. Chang, “An 
XMLEnabled Association Rule Framework,” Proc. 14th Int’l Conf. Database and Expert Systems Applications, pp. 88-97, 2003. 

[8] World Wide Web Consortium, XML Schema, http://, 2001.

[9]Chai, K.; H. T. Hn, H. L. Chieu; “Bayesian Online Classifiers for Text Classification and Filtering”, Proceedings of the 25th annual international ACM SIGIR conference on Research and Development in Information Retrieval, August 2002.

[10] DATA MINING Concepts and Techniques, Jiawei Han, Micheline Kamber Morgan Kaufman Publishers, 2003

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