Faculty Publications
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Item 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.Item 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.Item Ensemble deep neural network based quality of service prediction for cloud service recommendation(Elsevier B.V., 2021) Sahu, P.; Raghavan, S.; Chandrasekaran, K.Applications of Cloud Services are increasing day by day, and so is the difficulty of choosing the best-suited service for a customer. Quality of Service (QoS) parameters can be used for quality assurance and evaluation; further, a service can be recommended based on these QoS parameters’ values. Recommendation systems are getting much attention lately. It has a crucial role in almost all the major commercial platforms and many improvements are being made to make the recommendations more precise and closer to the user's requirements. Conventional Machine Learning algorithms and statistical analysis methods, presently are not that efficient in learning the complex correlation between data elements. Lately, Deep Learning models have proven to be practical and precise in areas like natural language processing, image processing, data mining, & data interpretation. However, there are not many examples of complete Deep Learning applications for cloud service recommendation systems, though some works partially use Deep Learning. We propose the Ensemble of Deep Neural Networks (EDNN) method, which is of the hybrid type, i.e., the fusion of neighborhood-based and neural network model-based methods. The output obtained from both the models are combined using another different neural network model. Our approach for predicting QoS values is simple and different from previous works, and the results show that it outperforms other classical methods marginally. © 2021 Elsevier B.V.