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• Sales Rank: #5829006 in Books
• Published on: 1978
• Number of items: 1
• Binding: Hardcover

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Develop key skills and techniques with R to create and customize data mining algorithms

About This Book

• Develop a sound strategy for solving predictive modeling problems using the most popular data mining algorithms
• Gain understanding of the major methods of predictive modeling
• Packed with practical advice and tips to help you get to grips with data mining

Who This Book Is For

This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.

What You Will Learn

• Discover how you can manipulate data with R using code snippets
• Get to know the top classification algorithms written in R
• Develop best practices in the fields of graph mining and network analysis
• Find out the solutions to mine text and web data with appropriate support from R
• Familiarize yourself with algorithms written in R for spatial data mining, text mining, and web data mining
• Explore solutions written in R based on RHadoop projects

In Detail

Being able to deal with the array of problems that you may encounter during complex statistical projects can be difficult. If you have only a basic knowledge of R, this book will provide you with the skills and knowledge to successfully create and customize the most popular data mining algorithms to overcome these difficulties.

You will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this book feeling confident in your ability to know which data mining algorithm to apply in any situation.

• Sales Rank: #2340849 in Books
• Published on: 2014-12-22
• Released on: 2015-01-31
• Original language: English
• Number of items: 1
• Dimensions: 9.25" h x .71" w x 7.50" l, 1.19 pounds
• Binding: Paperback
• 380 pages

About the Author

Bater Makhabel

Bater Makhabel (LinkedIn: BATERMJ and GitHub: BATERMJ) is a system architect living across Beijing, Shanghai, and Urumqi in China. He received his master's and bachelor's degrees in computer science and technology from Tsinghua University between the years 1995 and 2002. He has extensive experience in machine learning, data mining, natural language processing (NLP), distributed systems, embedded systems, the Web, mobile, algorithms, and applied mathematics and statistics. He has worked for clients such as CA Technologies, META4ALL, and EDA (a subcompany of DFR). He also has experience in setting up start-ups in China. Bater has been balancing a life of creativity between the edge of computer sciences and human cultures. For the past 12 years, he has gained experience in various culture creations by applying various cutting-edge computer technologies, one being a human-machine interface that is used to communicate with computer systems in the Kazakh language. He has previously collaborated with other writers in his fields too, but Learning Data Mining with R is his first official effort.

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19 of 20 people found the following review helpful.
Word salad
By Dimitri Shvorob
"Along with the nonstop accumulation of Internet documents, the difficulties in finding some useful information keeps increasing". The line gives you an idea of the author's grasp of English, and raises suspicions of plagiarism when one then sees articulate, correct, complex sentences. I googled a dozen uncharacteristically eloquent passages, and found two hits: the second bullet-point list from page 65 comes from "Encyclopedia of Data Warehousing and Mining" edited by Wang, and the CHARM algorithm pseudocode is copied from the original paper by Zaki and Hsiao. Where do the other ten come from?

Contrary to its title, "Learning Data Mining with R" is *absolutely* unsuitable for data-mining and R beginners, and does not even attempt a coherent introduction. Instead, one gets what looks like a sketchy set of notes listing the various algorithms, illustrated with probably-borrowed pseudocode and probably-original R code.

It looks like you are getting something, but you really aren't, because the pseudocode is not commented - it is not even typed up, but copy-pasted from the source as an image, hence the wide variation in styles - and the short R snippets are incomplete, e.g. call unknown functions. Oddly, the book features a lot of formulas; this is inappropriate for a popular beginner book, and people who can handle the math will get a proper textbook from a proper publisher.

I give two stars because the project did require considerable work, and an education, but it's still an "avoid".

PS. Yeah, right, two five-star reviews - by first-time reviewers - in one day, after I posted mine. "Outstanding", but no specifics, and silence when asked to elaborate.

UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.

11 of 11 people found the following review helpful.
Very disappointing
By Mario Hernandez Vazquez
I bought this book because I was interested in the text mining chapter (chapter 10) and when directed to the publisher’s webpage to get the R code… surprise, the code is available for chapters 2 thru 6. Then I wrote to the publisher to ask where I can find the code for chapter 10 and still haven’t got an answer. I’m very disappointed with this purchase, not what I was expecting.

10 of 10 people found the following review helpful.
Do Not Buy This Book
By Amazon Customer
This book is a catastrophe. The writing itself is dreadful, and far too little information is provided.

A much better book on this subject is "Machine Learning with R" (ISBN-13: 978-1782162148) by Brett Lantz, from the same publisher.

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• Sales Rank: #8950403 in Books
• Published on: 1707
• Binding: Hardcover

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Following the success of its best-selling predecessors, the Fourth Edition of Harriette Pipes McAdoo's Black Families retains several now classic contributions while including updated versions of earlier chapters and many entirely new chapters. The goal through each revision of this core text has been to compile a book that focuses on positive dimensions of African American families. The book remains the most complete assessment of black families available in both depth and breadth of coverage. Cross-disciplinary in nature, the book boasts contributions from such fields as family studies, anthropology, education, psychology, social work, and public policy.

• Sales Rank: #610891 in Books
• Brand: Brand: SAGE Publications, Inc
• Published on: 2006-08-10
• Original language: English
• Number of items: 1
• Dimensions: 9.00" h x .87" w x 6.00" l, 1.13 pounds
• Binding: Paperback
• 384 pages

Features

• Used Book in Good Condition

About the Author
Harriette Pipes McAdoo is a University Distinguished Professor at Michigan State University, Department of Family and Child Ecology.  Previously, she was Professor at Howard University in the School of Social Work and Visiting Lecturer at Smith College, the University of Washington, and the University of Minnesota.  She is a Director of the Groves Conference on Marriage and the Family; was a National Adviser to the President of the White House Conference on Families; was former President and Board Member of the National Council on Family Relations; and was a member of the Governing Council of the Society for Research in Child Development.  She was the first person honored by the National Council on Family Relations with the Marie Peters Award for Outstanding Scholarship, Leadership, and Service in the Area of Ethnic Minority Families.  Dr. McAdoo received her B.A. and M.A. from Michigan State University and her Ph.D. from the University of Michigan, and she has done post-doctoral studies at Harvard University.  She has published on racial attitudes and self-esteem in young children, Black mobility patterns, coping strategies of single mothers, and professional Kenyan women and HIV/AIDS in Zimbabwe.  She is editor of Black Children: Social, Educational, and Parental Environments, Second Edition (2002, SAGE) and Family Ethnicity: Strength in Diversity, Second Edition (1999, SAGE), as well as Young Families, Program Review, and Policy Recommendations.  She is coauthor of Women and Children, Alonge and in Poverty.  She has four children and four grandchildren.

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