Data Mining and Knowledge Engineering

J. Ragaventhiran ¹, G. Sharmila ², B. Muthu Kumar ¹

Affiliations
1 Department of Information Technology, Syed Ammal Engineering College, Ramanathapuram-623502, Tamilnadu, IN
2 Department of Computer Science and Engineering, Syed Ammal Engineering College, Ramanathapuram-623502, Tamilnadu, IN

Abstract

Privacy preservation is an important concept in the field of data mining approach to introduce the protection of dataset. Our work is to secure the datasets that are utilized for the data mining and mainly focuses on conversion of original datasets into unrealized datasets. During the conversion of unrealized datasets if any leakage occurs it should be recovered by cryptographic technique i.e., Advanced Encryption Standard (AES) may be identified to convert the unrealized datasets into unreadable format through encryption. Sample datasets are taken and stored for reuse also and recover the utility of original datasets data reconstruction is performed using. The proposed Fuzzy Decision Tree (FDT) algorithm is identified to reconstructs the original dataset which can be viewed for future use even if the samples lost.

Keywords

Data Mining, Machine Learning, Security, Prediction, Classification and Privacy Protection.

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S. Sakthi Nivetha ¹, K. Suganya ¹, J. Ragaventhiran ¹

Affiliations
1 Department of Information Technology, Syed Ammal Engineering College, Ramanathapuram, IN

Abstract

The database research field has concentrated on the Extensible Markup Language (XML) due to its flexible hierarchical nature which can use to represent huge amounts of data  also it does not have absolute and fixed schema, but having possibly irregular and incomplete structure. It is a very hard task to extract information from semi structured documents and is going to become more and more difficult as the amount of digital information available on the Internet grows. On querying the XML document directly, the problem of information overload may occur where too much data are included in the answer because the set of keyword specified for the search capture too many meanings and the problem of information deprivation where either the use of inappropriate keywords, or wrong formulation of query prevents the user from receiving the correct answer. In this work we describe an approach to mine Tree based Association Rules (TARs) from XML documents. Such rules provide information on both the structure and the content of the XML document and the rules can be stored in XML format for the purpose of querying. The mined knowledge is used to provide the quick, approximate answers to queries and information about structural regularities that can be used as data guides for document querying. Here, we propose an algorithm that extends CMTree Miner (discovers closed and maximal frequent subtrees) to mine tree based association rules from XML document.

Keywords

Extensible Markup Language (XML), Approximate Query Answering, Data Mining, Intensional Information, Tree-Based Association Rules.

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