Advanced Data Mining Tools and Methods for Social Computing
Lead Editor’s Name
Scope and Objectives of the Book
Social network has increased surprising consideration in the most recent decade and it turned into an extraordinary region of computer science that is pertained with the combination of social behaviour and computational systems. Nowadays, different kinds of social networks developed, like, Google+, Facebook, Twitter, scientific cooperation networks, airport passage networks, etc. Social network deals with huge volume of heterogeneous and unstructured data and they are very tough to handle. Due to increasing volume and popularity, one of the promising and attractive research areas becomes social network. Data Mining affirms to get information by finding patterns/correlations among data patterns. This data is applied in different applications like business, training, online life, clinical, agriculture, and so on. Data mining algorithms and tools are efficiently and effectively applied to handle social media data. This book means to give different novel and hybrid high quality data mining methodologies, techniques, algorithms, architectures, tools and methods will be presented to cope up the social network issues.
This publication tends to involve major emerging trends in technology which are supporting the current advancement of the social network with the help of data mining techniques and tools. It also aims to highlight advancement of the conventional approaches in the field of social networking. The scope of this publication involves proposing novel techniques and reviewing state-of-the-art in the area of data mining, machine learning, soft computing techniques, etc and also to relate the same to their applications in the field of social network. The motivation of this publication is not only to put forward new ideas in technology innovation but also to analyse the effect of the same in current context of social media analysis.
Topics included but no limited to:
Social Network Representation
Image Mining and Video Mining
Hidden Community Mining
Heterogeneous Social Networks
Network Representation Learning
Web Data Mining
Clustering and Classification
Association Rules and Sequential Patterns
Data Warehouse and OLAP
Mining Sequence Patterns
Mining Time-Series Data
Date of Receiving Chapter Proposal 30th June, 2020
Date of Notification of Chapter Acceptance 05th July, 2020
Date of Receiving of Full Chapter 31st August, 2020
Date of Notification of Chapter Acceptance 20th October, 2020
Date of Receiving Camera Ready Chapter 20th December, 2020
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