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Title: Mining and Analysis of Social Networks Sites
Researcher: Anupriya Jain
Guide(s): M K Sharma
University: Jagannath University
Completed Date: 05.02.2018
Abstract: The tremendous growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. newline newlineFrom an academic researcher perspective, studying social area networks, growth rates, and social implications of social networking sites is likely to draw strong willing for design and test new approaches of data mining from social area network sites either by owners or by users. newline newlineWe also, we intend to study the existing social media mining approaches, analyze them, and propose a new model based on Social Web mining techniques with a mix of soft computing techniques that shall be an improvement over the existing ones. Thus, we hope to improve the efficiency and usefulness of the extracted social network newline newlineThe main objective of this work is to design and test new hybrid algorithms based on soft computing approaches to build user groups in social networks based on user behavior analysis. In this thesis, new techniques for clustering, classification, and associations generation have been proposed for providing effective and fast mining to social networks related data. From the experimental results, it is found that the performance of this proposed algorithm is better than the other related algorithms. Hence, they are useful for web mining and
Appears in Departments:Faculty of Engineering and Technology

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01_title.docxAttached File17.4 kBMicrosoft Word XMLView/Open
02_candidate_declaration.docx11.56 kBMicrosoft Word XMLView/Open
03_certificate_of_the_supervisor.docx11.2 kBMicrosoft Word XMLView/Open
04_acknowledgments.docx11.62 kBMicrosoft Word XMLView/Open
05_preface.docx11.84 kBMicrosoft Word XMLView/Open
06_contents.docx15.89 kBMicrosoft Word XMLView/Open
07_list_of_tables.docx15.33 kBMicrosoft Word XMLView/Open
08_list_of_figures.docx16.37 kBMicrosoft Word XMLView/Open
09_abbreviations.docx11.83 kBMicrosoft Word XMLView/Open
10_chapter1.docx21.54 kBMicrosoft Word XMLView/Open
11_chapter2.docx66.91 kBMicrosoft Word XMLView/Open
12_chapter3.docx57.49 kBMicrosoft Word XMLView/Open
13_chapter4.docx916.12 kBMicrosoft Word XMLView/Open
14_chapter5.docx630.25 kBMicrosoft Word XMLView/Open
15_chapter6.docx164.82 kBMicrosoft Word XMLView/Open
16_chapter7.docx13.56 kBMicrosoft Word XMLView/Open
18_list_of_publication.docx10.7 kBMicrosoft Word XMLView/Open
18_references.docx28.61 kBMicrosoft Word XMLView/Open
19_reprint_of_research _paper.docx50.64 kBMicrosoft Word XMLView/Open

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