Pradnya Malaganve

Work place: Department of Computer Science, School of Computational Sciences and IT, Garden City University, Bengaluru, India



Research Interests: Knowledge Discovery, Data Mining


Ms. Pradnya Malaganve is currently pursuing Ph.D. in Computer Science at Garden City University, Bengaluru. She received Bachelor of Computer Applications degree from Karnataka University Dharwad in 2012. She received Master of Science degree from Rani Channamma University, Belagavi, in 2014. She has 4 years of teaching experience and she has published 5 research papers in various conferences and International journals Her research interest includes Data Mining and Knowledge Discovery, Web Mining, Web Multimedia Mining etc.

Author Articles
Metadata based Classification Techniques for Knowledge Discovery from Facebook Multimedia Database

By Prashant Bhat Pradnya Malaganve

DOI:, Pub. Date: 8 Aug. 2021

Classification is a parlance of Data Mining to genre data of different kinds in particular classes. As we observe, social media is an immense manifesto that allows billions of people share their thoughts, updates and multimedia information as status, photo, video, link, audio and graphics. Because of this flexibility cloud has enormous data. Most of the times, this data is much complicated to retrieve and to understand. And the data may contain lot of noise and at most the data will be incomplete. To make this complication easier, the data existed on the cloud has to be classified with labels which is viable through data mining Classification techniques. In the present work, we have considered Facebook dataset which holds meta data of cosmetic company’s Facebook page. 19 different Meta Data are used as main attributes. Out of those, Meta Data ‘Type’ is concentrated for Classification. Meta data ‘Type’ is classified into four different classes such as link, status, photo and video. We have used two favored Classifiers of Data Mining that are, Bayes Classifier and Decision Tree Classifier. Data Mining Classifiers contain several classification algorithms. Few algorithms from Bayes and Decision Tree have been chosen for the experiment and explained in detail in the present work. Percentage split method is used to split the dataset as training and testing data which helps in calculating the Accuracy level of Classification and to form confusion matrix. The Accuracy results, kappa statistics, root mean squared error, relative absolute error, root relative squared error and confusion matrix of all the algorithms are compared, studied and analyzed in depth to produce the best Classifier which can label the company’s Facebook data into appropriate classes thus Knowledge Discovery is the ultimate goal of this experiment.

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