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A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining

A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining
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Author(s): Shailendra Kumar Sonkar (National Institute of Technical Teacher's Training and Research, India), Vishal Bhatnagar (Ambedkar Institute of Advanced Communication Technologies and Research, India)and Rama Krishna Challa (National Institute of Technical Teacher's Training and Research, India)
Copyright: 2014
Pages: 18
Source title: Data Mining and Analysis in the Engineering Field
Source Author(s)/Editor(s): Vishal Bhatnagar (Ambedkar Institute of Advanced Communication Technologies and Research, India)
DOI: 10.4018/978-1-4666-6086-1.ch013

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Abstract

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.

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