Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Chandrasekaran, K.Subject: search.filters.subject.Data mining

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    A Privacy Preserved Data Mining Approach Based on k-Partite Graph Theory
    (Elsevier, 2015) Bhat, T.P.; Karthik, C.; Chandrasekaran, K.
    Traditional approaches to data mining may perform well on extraction of information necessary to build a classification rule useful for further categorisation in supervised classification learning problems. However most of the approaches require fail to hide the identity of the subject to whom the data pertains to, and this can cause a big privacy breach. This document addresses this issue by the use of a graph theoretical approach based on k-partitioning of graphs, which paves way to creation of a complex decision tree classifier, organised in a prioritised hierarchy. Experimental results and analytical treatment to justify the correctness of the approach are also included. © 2015 The Authors.
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    Intelligent Data Mining for Collaborative Information Seeking
    (Springer Nature, 2020) Kumar, A.; Chandrasekaran, K.; Shukla, A.; Usha, D.
    World Wide Web (WWW) contains different kinds of information whether it be social, educational, historical, sports, news, financial, weather, technology, politics etc. Most of the people spend time on the internet to access data for information seeking purposes. Information provided on the web is available in different formats like in text format, image format or video format, and they can be accessed through different access interfaces. Accessing information from such a large place i.e. World Wide Web through so many websites would become a very cumbersome process, therefore, in this paper, we present a new method which will produce information based on the user input using appropriate keywords. The data will be retrieved from the internet using Data Mining approach without the need for rules and training of pages. The main focus will be to extract or retrieve data of a person like educational qualifications, gender, contact information, contributions in his work, his/her social nature, etc. The query to be searched on the platform or model should have meaningful keywords attached to it best describing the person or else data of some different person might be fetched. © 2020, Springer Nature Switzerland AG.