{"id":8411,"date":"2018-02-17T09:30:27","date_gmt":"2018-02-17T04:00:27","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=8411"},"modified":"2021-05-28T13:02:05","modified_gmt":"2021-05-28T07:32:05","slug":"best-data-mining-books","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/best-data-mining-books\/","title":{"rendered":"15 Best Data Mining Books To Learn Data Mining"},"content":{"rendered":"
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In this blog, we will study Best Data Mining<\/strong><\/a> Books. Also, will learn the description of books. We will try to cover the best books for data mining.<\/p>\n

So, let’s explore best data Mining Books.<\/p>\n

\"Best<\/a><\/p>\n

Top Data Mining Books<\/h2>\n

1. Introduction to Data Mining<\/h3>\n

\"Introduction<\/a><\/p>\n

by Tan, Steinbach & Kumar\u00a0<\/em><\/p>\n<\/div>\n

\n
Basically, this book is a very good introduction book for data mining. It discusses all the main topics of data mining that are clustering<\/strong><\/a>, classification<\/strong><\/a>, pattern mining, and outlier detection. Moreover, it contains two very good chapters on clustering by Tan & Kumar.<\/div>\n
<\/div>\n
The book gives both theoretical and practical knowledge of all data mining topics. It also contains many integrated examples and figures. Every important topic is presented into two chapters, beginning with basic concepts that provide the necessary background for learning each data mining technique, then it covers more complex concepts and algorithms.<\/div>\n<\/div>\n
<\/div>\n

2. An Introduction to Statistical Learning: with Applications in R<\/h3>\n

\"An<\/a>by Gareth James & Daniela Witten<\/em><\/p>\n<\/div>\n

\n
Overview of statistical learning based on large datasets of information. The exploratory techniques of the data are discussed<\/span> using the R programming language<\/strong><\/a>.\u00a0The book provides important prediction and modeling techniques, along with relevant applications. It includes topics like linear regression, classification, clustering, shrinkage approaches, resampling methods,tree-based methods, support vector machines. Color graphics and real-world examples illustrate the methods presented.<\/div>\n<\/div>\n
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<\/h3>\n<\/div>\n

3. Data Science for Business: What you need to know about data mining and data-analytic thinking<\/span><\/h3>\n

\"Data<\/a>by Foster Provost & Tom Fawcett<\/em><\/p>\n

\n
Generally, an introduction to data science principles and theory. Also, it explains the necessary analytical thinking to approach this kind of problem. Further, it discusses various data mining techniques<\/strong><\/a> to explore information.<\/div>\n
<\/div>\n
You will learn to visualize business problems data-analytically by using the data-mining process to collect good data in the appropriate way. The book will help you understand general concepts for gaining knowledge from data.<\/div>\n<\/div>\n
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<\/h3>\n<\/div>\n

4. Modeling With Data<\/h3>\n

\"Modeling<\/a>by Ben\u00a0 Klemens<\/em><\/p>\n

\n
This book focuses on some processes to solve analytical problems applied to data. In particular, it explains the theory to create tools. That is for exploring big datasets of information.<\/div>\n
<\/div>\n
The book also offers a narrative to the necessary points about statistics, although it directly implies that this book is incomplete relative to all the encyclopedic texts.<\/div>\n<\/div>\n
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<\/h3>\n<\/div>\n
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5. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners<\/span><\/h3>\n

\"Big<\/a>by Jared Dean<\/em><\/p>\n

\n
On this resource, the reality of big data<\/strong><\/a> is explored<\/span>, and its benefits, from the marketing point of view. It also explains how to store this kind of data and algorithms to process it. As it was based<\/span> on data mining<\/strong><\/a> and machine learning<\/strong><\/a>.<\/div>\n
<\/div>\n
The book provides the description of big data and its characteristics, information on high-performance computing architectures for analytics, huge parallel processing (MPP) and in-memory databases, brief coverage of data mining, machine learning algorithms, and text analytics<\/div>\n<\/div>\n
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<\/h3>\n

6. Data Mining: Practical Machine Learning Tools and Techniques<\/h3>\n<\/div>\n

\"Data<\/a>by Ian H. Witten & Eibe Frank<\/em><\/p>\n

\n
Basically, it is full of real-world situations where machine learning tools are applied<\/span>. Also, this is a practical book which provides you the knowledge. Also, has the ability to master the whole process of machine learning<\/a><\/strong>.<\/div>\n
<\/div>\n
The book offers a complete grounding in machine learning concepts as well as practical tips on implementing the tools and techniques to your data mining projects. It also provides strong tips and strategies for performance improvement that work by modifying the input or output in machine learning methods.<\/div>\n

<\/h3>\n<\/div>\n

7. Mining the Social Web<\/h3>\n

\"Mining<\/a>by Matthew A. Russell<\/em><\/p>\n

\n
<\/div>\n
\n
Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More.<\/div>\n
<\/div>\n<\/div>\n
\n
The exploration of social web data is explained<\/span> in this book. Data capture from social media apps. Also, it\u2019s manipulation and the final visualization tools are the focus of this resource.<\/div>\n
<\/div>\n
The book provides an accurate synopsis of the social web landscape, the usage of Docker to smoothly run each chapter\u2019s example code, packaged as a Jupyter notebook, understanding the process of adapting and contributing to the code\u2019s open source GitHub repository.<\/div>\n<\/div>\n

8. Probabilistic Programming & Bayesian Methods for Hackers<\/h3>\n<\/div>\n

\"Bayesian<\/a>by Cameron Davidson & Pilon<\/em><\/p>\n

\n
A book about Bayesian networks that provide capabilities to solve very complex problems. Also discusses programming implementations on the Python language<\/strong><\/a>.<\/div>\n
<\/div>\n
You will understand the usage of the Markov Chain Monte Carlo algorithm, how to choose perfect sample sizes and priors, work in loss functions, and implement Bayesian inference in domains ranging like finance and marketing.<\/div>\n

<\/h3>\n<\/div>\n

9. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management<\/h3>\n

\"Data<\/a>by Gordon S. Linoff & Michael J. A. Berry<\/em><\/p>\n

\n
<\/div>\n
\n
A data mining book oriented specifically<\/span> to marketing and business management. With great case studies in order to<\/span> understand how to apply these techniques in the real world.<\/div>\n
<\/div>\n
The book includes a new data mining technique in all chapters along with clear and short explanations on the process to execute each technique.<\/div>\n
It features major data mining techniques, like link analysis, decision trees, collaborative filtering, neural networks, survival analysis, and association rules.<\/div>\n<\/div>\n
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10. Inductive Logic Programming Techniques and Applications<\/h3>\n<\/div>\n

\"Inductive<\/a>by Lavrac N.<\/em><\/p>\n<\/div>\n<\/div>\n

\n
An old book about inductive logic programming with great theoretical and practical information. Also, referencing some important tools.<\/div>\n
<\/div>\n
The author focuses on inductive logic programming with its uses and in the first few chapters, provides details of the theoretical foundations of the subject. They characterize machine learning paradigms as deductive, inductive, learning with neural nets and learning with genetic algorithms.<\/div>\n<\/div>\n
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<\/h3>\n<\/div>\n

11. Mining of Massive Datasets<\/h3>\n

\"Mining<\/a>by Anand Rajaraman & Jeffrey David Ullman<\/em><\/p>\n

<\/div>\n
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The main focus of this data mining book is to provide the necessary tools and knowledge to manage, manipulate. Also, consume large chunks of information into databases.<\/div>\n
<\/div>\n
The authors describe the techniques of locality-sensitive hashing and stream processing algorithms for mining data that comes very fast for exhaustive processing. Then, it covers the PageRank idea and tricks for Web organizing. Other chapters focus on the issues of finding frequent itemsets and clustering. The last chapters include two applications, Web advertising, and recommendation systems, both important in e-commerce.<\/div>\n<\/div>\n

12. Data Mining: The Textbook<\/h3>\n

\"Data<\/a><\/p>\n

by Charu C. Aggarwal<\/em><\/p>\n

\n
This is probably<\/span> one of the top data mining books that I have read recently for a computer scientist. It also covers the basic topics of data mining but also some advanced topics.<\/div>\n
<\/div>\n
Moreover, it is very up to date, being a very recent book. It is also written by a top data mining researcher (C. Aggarwal). It also covers many recent and advanced topics. Such as time series, graph mining and social network mining.<\/div>\n<\/div>\n
\n

<\/h3>\n<\/div>\n

13. A Programmer\u2019s Guide to Data Mining<\/h3>\n

\"A<\/a>by Ron Zacharski<\/em><\/p>\n

\n
A guide through data mining concepts in a programming point of view. It provides several hands-on problems that need to practice and tests the subjects taught in this online book.<\/div>\n<\/div>\n
<\/div>\n
The book includes chapters like, get started with recommendation systems, implicit ratings and item-based filtering, further explorations in classification, naive bayes, naive bayes, and unstructured texts and, clustering.<\/div>\n
\n

<\/h3>\n<\/div>\n

14. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery<\/h3>\n

\"Data<\/a>by Graham Williams<\/em><\/p>\n

\n
The objective<\/span> of this book is to provide you lots of information on data manipulation. Also, it focuses on the Rattle toolkit. Moreover, the R language<\/strong><\/a> to demonstrate<\/span> the implementation of these techniques.<\/div>\n
<\/div>\n
The book includes data understanding, model evaluation, data refinement, data preparation, model building, and practical deployment. You will learn to instantly deliver a data mining project through software that is easy to install for free from the Internet. Coupling Rattle with R provides a data mining environment with all the power, and more, of the many commercial offerings.<\/div>\n<\/div>\n
<\/div>\n

15. The Elements of Statistical Learning<\/h3>\n

\"The<\/a>by Trevor Hastie & Robert Tibshirani<\/em><\/p>\n

Basically, this is a quite popular book a little bit more focused on statistics. Also, it covers both many data mining techniques. Such as Neural networks, association rule mining, SVM, regression, clustering and other topics. What is interesting about this book is that it is a top book used in many university courses like the other.<\/p>\n

The author covers many topics, like graphical models,ensemble methods, least angle regression, random forests, & path algorithms regarding the lasso, non-negative matrix factorisation.<\/p>\n

So, this was all about Best Data Mining Books. Hope you like our explanation.<\/p>\n

\n

Summary<\/h2>\n<\/div>\n

As a result, we have studied the best Data Mining Books. We have covered books which are best for freshers as well as experienced also. Furthermore, if you have any query feel free to ask in a comment section.
\nSee Also- Data Mining Applications and Use Cases<\/strong><\/a><\/p>\n

<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

In this blog, we will study Best Data Mining Books. Also, will learn the description of books. We will try to cover the best books for data mining. 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