- Series: Advances in Database Systems (Book 40)
- Hardcover: 600 pages
- Publisher: Springer; 2010 edition (February 19, 2010)
- Language: English
- ISBN-10: 1441960449
- ISBN-13: 978-1441960443
- Product Dimensions: 6.1 x 1.4 x 9.2 inches
- Shipping Weight: 2.3 pounds (View shipping rates and policies)
- Average Customer Review: 22 customer reviews
- Amazon Best Sellers Rank: #2,987,488 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Managing and Mining Graph Data (Advances in Database Systems) 2010th Edition
Use the Amazon App to scan ISBNs and compare prices.
The Amazon Book Review
Author interviews, book reviews, editors picks, and more. Read it now
Frequently bought together
Customers who viewed this item also viewed
From the reviews:“This book provides a survey of some recent advances in graph mining. It contains chapters on graph languages, indexing, clustering, pattern mining, keyword search, and pattern matching. … The book is targeted at advanced undergraduate or graduate students, faculty members, and researchers from both industry and academia. … I highly recommend this book to someone who is starting to explore the field of graph mining or wants to delve deeper into this exciting field.” (Dimitrios Katsaros, ACM Computing Reviews, December, 2010)
From the Back Cover
Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing.
Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science.
About the Editors:
Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has worked as a researcher at IBM since then, and has published over 130 papers in major data mining conferences and journals. He has applied for or been granted over 70 US and International patents, and has thrice been designated a Master Inventor at IBM. He has received an IBM Corporate award for his work on data stream analytics, and an IBM Outstanding Innovation Award for his work on privacy technology. He has served on the executive committees of most major data mining conferences. He has served as an associate editor of the IEEE TKDE, as an associate editor of the ACM SIGKDD Explorations, and as an action editor of the DMKD Journal. He is a fellow of the IEEE, and a life-member of the ACM.
Haixun Wang is currently a researcher at Microsoft Research Asia. He received the B.S. and the M.S. degree, both in computer science, from Shanghai Jiao Tong University in 1994 and 1996. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He subsequently worked as a researcher at IBM until 2009. His main research interest is database language and systems, data mining, and information retrieval. He has published more than 100 research papers in referred international journals and conference proceedings. He serves as an associate editor of the IEEE TKDE, and has served as a reviewer and program committee member of leading database conferences and journals.
Browse award-winning titles. See more
Top customer reviews
There was a problem filtering reviews right now. Please try again later.
This book did the job perfectly as it captures both R and data mining, and even though some may argue it is at a somewhat basic level, I think for people looking to transition into R, this is the best guide they will find.
I love learning new languages using a basic step practical examples. This book will not teach you the most complicated techniques used in data mining, but I never expected it to. I just wanted to know what to use to import the data, run analysis, visualize various aspects of it and then export or apply results.
Again for people coming from MatLab or Octave, this is a great book! (worked great for me)