Context based text document sharing system using association rule mining

In today's document sharing environment, when documents are shared over a group of people, document context of document and context of user is not considered. Therefore sometimes it may happen that the document may get delivered to unintended user over the network. This leads to unnecessary transfer of document. To reduce this document transfer overhead, we are proposing a system that will consider document context as well as user context. By using these both of the contexts, document will get transferred to only intend user. This will also reduce time overhead to transfer a document to a group of peoples, because users belong to different context than document context will be eliminated. To identify document context and user context, we proposed two models Constant Weight Distribution Model and Common Words Probability Model. We also proposed a context dictionary to store different contexts and associated terms with them.

[1]  Anil K. Jain,et al.  Classification of text documents , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[2]  Ryen W. White,et al.  Predicting user interests from contextual information , 2009, SIGIR.

[3]  Sourav S. Bhowmick,et al.  Association Rule Mining: A Survey , 2003 .

[4]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[5]  S. K. Singh,et al.  A Novel Context Based Indexing of Web Documents , 2012, 2012 International Conference on Communication Systems and Network Technologies.

[6]  T. Martin McGinnity,et al.  A Context-Based Word Indexing Model for Document Summarization , 2013, IEEE Transactions on Knowledge and Data Engineering.

[7]  S. Biruntha,et al.  Techniques on text mining , 2012, 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT).

[8]  Tudor Cioara,et al.  A context - based semantically enhanced information retrieval model , 2009, 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing.

[9]  V. Tokekar,et al.  Identifying context of text documents using Naïve Bayes classification and Apriori association rule mining , 2012, 2012 CSI Sixth International Conference on Software Engineering (CONSEG).

[10]  Dimitris Kanellopoulos,et al.  Association Rules Mining: A Recent Overview , 2006 .

[11]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[12]  Parag Kulkarni,et al.  Learning Context for Text Categorization , 2011, ArXiv.

[13]  Yongik Yoon,et al.  A Meta Data Model of Context Information for Dynamic Service Adaptation on User Centric Environment , 2007, 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07).