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Dear Sir/Madam,


I trust this e-mail finds you well! You are receiving this e-mail because of your expertise in the field of data mining and its applications in social network analysis and fuzzy systems. As such, I would like to cordially invite you to consider contributing your expertise to a forthcoming book edited by me entitled Data Mining in Dynamic Social Networks and Fuzzy Systems.


With the proliferated use of social networks in today’s modern era, data mining has found its significant place in social network analysis and its security. Privacy preservation of social networks is a brewing topic of research these days. With large amounts of content being posted on social networks, privacy breach has become one of the prime issues of social networking. Data mining techniques like classification, clustering, and association rule mining has been used extensively for social network analysis and data mining techniques like heuristics based, reconstruction based, and cryptographic techniques that are being applied on social networks for providing desired security. There are also various anonymization techniques like clustering and clustering with constraints that make use of data mining to provide privacy preservation of released social network data. Much less work has been done in the area of dynamic social network security and is a focus of study for various researchers and practitioners. Social networks contain a vast amount of information and such high dimensional data are difficult to be handled by the traditional systems like OLAP. For this, data mining has proved to be a blessing in disguise. Various data mining and statistical techniques find its use in analyzing large amounts of online (Dynamic) social network data, where the interactions among the users of the network are studied to find out interesting patterns and also to find out various outliers in the data.


The use of fuzzy logic can be intervened with data mining so as to give another dimension to the concept of data mining. The fuzzy logic is used in data mining to create a new concept called the fuzzy data mining which makes data mining more flexible and extends its utility by a large extent in fields such as intrusion detection, approximations of missing values, power plant optimizations, human resource management, cross-selling, detection of quality of water, decision making or medical image processing. The fuzzy logic theory brings a paradigm in work with the graduation, uncertainty and ambiguity described by linguistic expressions which uses knowledge that does not have clearly defined boundaries. Fuzzy theory is useful for data mining systems performing rule based classification. It provides operations for combining fuzzy measurements. The future prospect of data mining in the field of fuzzy data mining would be revealing applications on fuzzy sets. The research will be focused on finding the utilization of other techniques of data mining in fuzzy systems like genetic algorithms. The focus of the study will also be on how more effective results can be drawn on application of data mining on fuzzy systems. Considering above facts, there exist a need for an edited collection of chapters in this area.



As you have a specific interest in said field, I would like to invite you to contribute a chapter on this topic.  However, please know that your contribution would not be limited to that topic; should you be so inclined, please feel free to write your chapter on any of the other following topics instead:


·         Mining social interactions for viral marketing

·         Data preparation and pre-processing for mining of large social networks

·         Data mining techniques for community discovery in social networks

·         Trend prediction in evolving social networks

·         Discovering temporal patterns in social networks

·         Mining stream data in evolving social networks

·         Contextual social network analysis

·         High performing algorithms for social network mining

·         Graph search algorithms on social networks

·         Data mining for security in social networks

·         Real-time mining of social networks

·         Data mining for malware analysis in social networks

·         Data mining in semantic web platforms for the social web

·         Data mining for security in social networks

·         Data mining for malware analysis in social networks

·         Data mining and knowledge discovery in natural languages for social networks

·         Applications of data mining in social network analysis

·         Preparing data for dynamic social network mining

·         Large-scale graph mining algorithms

·         Fuzzy clustering

·         Fusion of neural networks and fuzzy systems

·         Feature selection, dimension reduction, pattern classification and recognition with fuzzy systems

·         Fuzzy evolutionary computing

·         Fuzzy pattern recognition

·         Fuzzy neural systems, Neuro-fuzzy systems

·         Fuzzy-rule based system

·         Applications of data mining in fuzzy systems

·         Data mining for Fuzzy Analysis

·         Fuzzy Genetic Algorithms

·         Fuzzy clustering

·         Fuzzy data analysis

·         Mining semantic web data from social software applications

·         Scalability in mining social networks

·         Data mining and knowledge discovery in natural languages for social networks

·         Data mining in mobile social networks

·         Mining semantic web data from social software applications

·         Scalability in mining social networks

·         Evolution of communities in the Web




Should you accept this invitation, I would like to kindly ask that, on or before June 1st , 2012, you submit via e-mail a 2-3 page chapter proposal for review that clearly explains the mission and concern of your proposed chapter.  Should your proposal be accepted, you will be notified by June 15th , 2012, and given until August 31st, 2012, to submit your chapter upon which it will be sent for double-blind peer review. This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the “Information Science Reference” (formerly Idea Group Reference), “Medical Information Science Reference,” “Business Science Reference,” and “Engineering Science Reference” imprints. For additional information regarding the publisher, please visit www.igi-global.com. This publication is anticipated to be released in 2013. Please visit http://bit.ly/IdTjVl for more details regarding this publication.


If you have any questions or concerns, please do not hesitate to contact me.  I appreciate your consideration of this invitation and hope to hear from you soon!


Best wishes,
Dr.Vishal Bhatnagar
B.E(CSE), M-Tech(IT), Ph.D
Associate Professor(CSE)
Ambedkar Institute of Advance Communication Technologies & Research
(Formely Ambedkar Institute of Technology)
Govt. Of NCT of Delhi
Geeta Colony
PhNo:+91-11-22405027, +91-9810460676
Email:[log in to unmask],[log in to unmask]

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