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Data Mining and Anlaytics are the foundation technologies for the new knowledge based world where we build models from data and databases to understand and explore our world. Data mining can improve our business, improve our government, and improve our life and with the right tools, any one can begin to explore this new technology, on the path to becoming a data mining professional. This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built. This is the first step in a journey to data mining and analytics. The book encourages the concept of programming by example and programming with data - more than just pushing data through tools, but learning to live and breathe the data, and sharing the experience so others can copy and build on what has gone before. It is accessible to many readers and not necessarily just those with strong backgrounds in computer science or statistics. Details of some of the more popular algorithms for data mining are very simply and, more importantly, clearly explained. Technology for transforming a database through data mining and machine learning into knowledge is now readily accessible.
- Sales Rank: #458344 in Books
- Published on: 2011-08-04
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .93" w x 6.10" l, 1.55 pounds
- Binding: Paperback
- 374 pages
Review
From the book reviews:
“The text does a great job of showing how to do each step using the data mining tool Rattle and related R concepts as appropriate. This makes it a great tool for someone who does not know much about R and wants to learn more about the powerful options available in R for data mining.” (Roger M. Sauter, Technometrics, Vol. 54 (3), August, 2012)
“This text is a manual for the impressive Rattle graphical user interface (GUI) for R, describing both the use of the GUI and the R code that is invoked to carry out the computations. … Data analysts … are likely to find Rattle a helpful tool that will allow them to quickly become productive with R. … There is extensive useful practical advice on data preparation and data manipulation. … is well suited for use in intermediate level courses on regression or classification.” (John H. Maindonald, International Statistical Review, Vol. 80 (1), 2012)
From the Back Cover
Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms.
Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing.
The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.
About the Author
Dr Graham Williams is Senior Director of Analytics with the Australian Taxation Office, and previously Principal Computer Scientist for Data Mining with CSIRO. He is also Visiting Professor and Senior International Scientist with the Shenzhen Institutes of Advanced Analytics of the Chinese Academy of Sciences, Adjunct Professor, Data Mining, Fraud Prevention, Security, University of Canberra, and Adjunct Professor, Australian National University. Graham regularly teaches data mining courses and is author of the freely available, open source data mining system, Rattle. He has been involved in many data mining projects for clients from government and industry over his long career. His research developments included ensemble learning (1980's) and hot spots discovery (1990's). He is actively involved in the international artificial intelligence and data mining research communities, particularly as chair of the Pacific Asia Knowledge Discovery and Data Mining conference series and founder and co-chair of the Australasian Data Mining conference series. Graham has editted a number of books and authored many academic and industry papers and reports. His current focus is on making data mining technology readily accessible, ensuring research, innovation and discovery are repeatable and available, and encouraging the free and open sharing of knowledge.
Most helpful customer reviews
29 of 32 people found the following review helpful.
No longer a sensible choice
By Dimitri Shvorob
... It's January 2014, and I feel that better books have come out since I posted the original review. Unless you for some reason have a specific interest in the R-based Rattle GUI, I would suggest looking at "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani, or "Machine learning with R" by Lantz.
17 of 20 people found the following review helpful.
What a great find !!!
By G3N1U5
Having read the book from cover to cover, and tried and completed all the examples, I would have to say I am a much better R programmer than I was before. The Beauty of this book, besides the simple yet informative language usage, is that when read in conjunction with the software Rattle Gui's operation, every thing was so simple to do. What made this even more relevant to me, was that the context and examples, about data, data mining and analytics, helped put every thing into perspective.
In summary, I found the book very readable, the examples easy to follow, and the explanations and reasons for why different processes are done. The book has helped make me a better data miner, and a better programmer in R. I was also pleased to discover that the book leads you to the authors website, which has a range of exercises as an extension to the book.
Finally the best of all, is that Rattle is open source and free, and with its inbuilt teaching facilities as in the log tab, I was able to continue my own professional development.
7 of 7 people found the following review helpful.
An excellent Data Mining and R primer
By File Under Common Knowledge
Data Mining with Rattle and R is an excellent book. The author has put a graphical shell on top of the R language, and structured it around the main steps of the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology. If you have a set of data that is reasonably well prepared (Rattle does provide some useful options to transform data, but it's not the answer to this thorny problem)then you can be up-and-running and experimenting with data almost immediately.
One important element of Rattle is that it is an 'R' code snippet generator. You express what you want to do and it produces the code (and there are a good number of configuration options to customise it). However, you can then take that code back into 'R' and modify and improve it to your heart's content: you are not constrained (or limited) by Rattle's high-level approach. This is a great way of learning a new language, especially 'R' which has over 4000 independently developed packages running on top of it (Williams also does an excellent job of highlighting and including the analytics and visualisation packages which have proved their worth to the community, and uses them from within Rattle).
His style of writing is clear, and his section on Building Models (including cluster analysis, association analysis, decision trees, random forests, boosting and support vector machines) is particularly well structured.
The worked examples do have a number of errors in them. I was particularly frustrated by the occasional inability to take the Rattle generated R code and run it natively inside RStudio. Further, some of the database connectivity doesn't work (I wanted to use SQL Server, which is currently broken), but you can't reasonably expect one individual - in their spare time - to support myriad storage products.
This book is excellent, and Rattle should be considered by any commercial organisation who is wanting to get a Data Mining team up-and-running very quickly and effectively, and to produce models for taking into production. It is not a substitute for subject-matter expertise and does not address handling "Big Data" volumes of information, but is a highly effective way of getting a team started.
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