Drawn from the US National Science Foundations Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field. Gathering perspectives from top experts across different disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.
Cited By
Patwary M, Palsetia D, Agrawal A, Liao W, Manne F and Choudhary A Scalable parallel OPTICS data clustering using graph algorithmic techniques Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, (1-12)- Patwary M, Palsetia D, Agrawal A, Liao W, Manne F and Choudhary A A new scalable parallel DBSCAN algorithm using the disjoint-set data structure Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, (1-11)
- Khan A, Muhammad A and Enriquez A Mining for Norms in Clouds Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing, (327-332)
- Ashok V and Mukkamala R Data mining without data Proceedings of the 10th annual ACM workshop on Privacy in the electronic society, (159-164)
- Grossman R and Gu Y Data mining using high performance data clouds Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, (920-927)
Index Terms
- Next Generation of Data Mining
Recommendations
Mining uncertain data
As an important data mining and knowledge discovery task, association rule mining searches for implicit, previously unknown, and potentially useful pieces of information—in the form of rules revealing associative relationships—that are embedded in the ...
Mining fuzzy specific rare itemsets for education data
Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may ...
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test ...
Reviews
Kalman Balogh
This book is the seventh in the publisher's "Data Mining and Knowledge Discovery" series. The previous books in the series focused on special methods or application areas of data mining. By contrast, this book collects papers about emerging research areas in data mining, influenced by grand challenges of engineering. It is an edited volume of the Next Generation of Data Mining Symposium, held in Baltimore in October 2007. The editors of this book are famous in the field of data mining-one of them, Vipin Kumar, is the series' editor. They are active organizers and participants in conferences and symposiums, professors at US universities, editors of journals in the field, and some of them are authors of books on statistics, data analysis, knowledge discovery, and data mining. Papers from more than 70 contributors are placed into one of the five chapters of the book, each representing one of these emerging areas of data mining: data mining in e-science and engineering; ubiquitous, distributed, and high-performance data mining; Web, semantics, and text mining; data mining in security, surveillance, and privacy protection; and medical, social science, financial, and spatial data mining. According to its originating symposium, the book does not intend to cover all the challenges, emerging application fields, and methods of data mining; however, it contains a very rich set of papers that document research in these areas. Some application areas-in the social sciences, biology, and medicine-are treated in papers placed in different chapters. Although a global index helps the reader locate in the book new phrases in the field, it seems to be rather ad hoc-it does not cover all the methods, techniques, and application areas mentioned. Some papers are very brief, only summarizing the results achieved, the problems, and the state of the research. Other papers are more comprehensive, describing interesting details of the problem and the proposed method and solution. Each paper ends with a long list of references, where the details of the research and the systems implemented are described. No commercial products are mentioned. I recommend the book to those who are interested in the newly emerging or at least cleverly modified/combined methods of data analysis, in order to solve problems in challenging new application areas that involve the handling of not-only-numeric, heterogeneous, dynamic/volatile, distributed, voluminous, fast-growing data. Online Computing Reviews Service
Access critical reviews of Computing literature here
Become a reviewer for Computing Reviews.