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Managing and Mining Graph Data (Advances in Database Systems), by Charu C. Aggarwal
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Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of 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 well known researchers in the field, 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 and engineering.
- Sales Rank: #2040423 in eBooks
- Published on: 2013-06-04
- Released on: 2013-06-04
- Format: Kindle eBook
Review
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.
About the Author
Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for or been granted over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE.
Most helpful customer reviews
109 of 112 people found the following review helpful.
Taken over by competition
By Dimitri Shvorob
It's January 2014 - and I am glad that better books have come out since I posted the original review, and one no longer has to accept CRC Hall's greedy pricing, and pay $65 for what really is a pretty imperfect book just because there is no choice. I'd say - pass on "Data mining with R", and go for "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani if you want a high-quality, accessible R-illustrated textbook, or for "Machine learning with R" by Brett Lantz if you are eager to jump into hacking, and value code over theory.
31 of 31 people found the following review helpful.
Excellent guide with real world case studies
By Ravi Aranke
If you are on a journey to become a data scientist, do yourself a favor and pick up a copy of this book.
Since R is an open source language with a strong community, there is no dearth of information and tutorials which will help the beginner quickly get up to speed (I highly recommend 'R Cookbook' by Paul Teetor).
What was lacking, in my opinion, was a book targeted at practitioners. A book which you can pick up and start using R in your work. A book which will compress the learning curve and equip you for real world mastery - to the point where, perhaps, you might head straight to Kaggle.com and take part in data mining competitions.
The book by Luis Torgo admirably fills this gap. In the context of the case studies, the author painstakingly describes the challenges one would face in real life - such as - how to go about cleaning and munging the data, how to visualize and summarize the data, how to come up with plausible hypothesis and test them. Since data mining is as much art as science, this kind of approach where you see an expert in action and see how they go about making design choices is highly educational.
Along the tour, you also learn about several popular add-on libraries such as xts, rocr and hmisc.
Once again, an excellent how-to book and highly recommended as your 2nd R book.
Ravi Aranke (longtaildata.com)
27 of 29 people found the following review helpful.
Invaluable resource for data miners
By Sandro Saitta
The book starts with an Introduction to R. Nicely written, it explains concepts that are needed to use this programming language for data mining. The book is then divided in four case studies. Each case study introduces data mining concepts that are illustrated using R.
First, pre-processing and data visualization are introduced through the prediction of algae blooms. Second, come the modelling and time ordering with the stock market application. Then, outlier detection and clustering are presented through fraud detection. Finally, feature selection and cross-validation are introduced through the classification of microarray samples. There is no introduction to data mining, but it's not a problem since concepts are explained through the different case studies.
Theoretical concepts are always linked to examples. This is the case for most of the data mining books. Luis goes a step further by linking each application to the corresponding code in R. It is thus easy to both understand a concept as well as implementing it with R. This is certainly one of the best book for a direct implementation of data mining algorithms. Another good point of the book is that for most of the problems there are different ways to solve them.
I have one remark regarding the stock market prediction chapter. I have already discussed this issue when I was working in finance. The author states that the percentage of profitable trades should be above 50% to have a successful trading strategy. This is not always the case. Imagine a system where each winning trade brings $2 while loosing trades costs $1. Since you can earn more money with winning trades than what you loose with loosing trades, you can thus still have a successful trading strategy with 48% of winning trades, for example.
As a conclusion, this is an invaluable resource for data miners, R programmers as well as people involved in fields such as fraud detection and stock market prediction. If you're serious about data mining and want to learn from experiences in the field, don't hesitate!
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