Best Reading Books Data Mining [2023]
The data mining books show us how data mining is a process that analyses large amounts of information and is called knowledge discovery in data. It involves six commonly available anomaly detection classes: clustering, regression, and summarization. Data mining helps in spam filtering and detecting fraud. and customer analysis.
The list of books we have provided below will give the readers a clear understanding of Data mining. These are essential reads for anyone who wants to increase their knowledge about the subject or requires to know more about it for career purposes.
Below is the list of the top ten books for professionals and beginners to enhance their understanding of Data mining.
| Sr. No. | Books | Author | Published | Rating (out of 5) |
| 1 | Data Mining and Predictive Analytics | Daniel T. Larose | 24 Apr 2015 | Amazon: 4.4
Goodreads: 3.50 |
| 2 | Data Mining: Concepts and Techniques | Jiawei Han, Jian Pei, Micheline Kamber | 1 Jan 2007 | Amazon: 4.6
Goodreads: 3.88 |
| 3 | Data Mining Techniques | Arun K Pujari | 2001 | Amazon: 4.3
Goodreads: 3.83 |
| 4 | Introduction to Data Mining | Pang Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar | 30 May 2021 | Amazon: 4.0
Goodreads: 3.00 |
| 5 | Big Data, Data Mining, and Machine Learning | Jared Dean | 2014 | Amazon: 4.3
Goodreads: 4.00 |
| 6 | Data Mining Techniques | Gordon S. Linoff, Michael J.A. Berry | 1 Jan 2012 | Amazon: 4.4
Goodreads: |
| 7 | Data Mining | Ian. H. Witten, Mark. A. Hall, Cristopher. J. Pal, Eibe Frank | 1 Jan 2019 | Amazon: 4.5
Goodreads: 4.00 |
| 8 | Insight Into Data Mining: Theory and Practice | K.P. Soman, Shyam Diwakar, V. AJay | 1 Jan 2006 | Amazon: 3.8
Goodreads: |
| 9 | Introduction to Data Mining with Case Studies | G.K. Gupta | 1 Jan 2014 | Amazon: 4.2
Goodreads: |
| 10 | Data Mining and Data Warehousing: Principles and Practical Techniques |
Parteek Bhatia |
27 June 2019 | Amazon: 4.6
Goodreads: |
Now we will go through the reviews individually.
Book #1: Data Mining and Predictive Analytics
Author: Daniel T. Larose
Get the book here.
Review:
An important book allows students to learn data mining and its different methods using real-world examples. The author introduces various models of predictive analytics, like multivariate analysis, neural networks, association rules, and logistic regression, to name a few. Every chapter offers problems and analytical solutions to aid the reader’s understanding of the subject. This book is a perfect guide for those pursuing an MBA or degree in statistics or even those chief executives undergoing corporate training.
Key Takeaways from that book
- The author offers detailed explanations of different association rules, logistic regression, and regression methods.
- The book includes case studies that help students improve their knowledge.
- The content has numerous exercises and assessment tests under every chapter.
Book #2: Data Mining: Concepts and Techniques
Author: Jiawei Han, Jian Pei, Micheline Kamber
Get the book here.
Review:
The concept of data and data analytics is necessary to acquire to flourish in the corporate world. Data Mining is an integral part of Data Science, and this book helps students learn about this concept in great detail. This latest edition covers new topics: Mining Stream, Mining Spatial, Cube Technology, and other complex concepts. The book is written in clear and simplistic language to help students further their skills in Data Mining.
Key Takeaways from that book
- Every chapter features a detailed explanation, algorithms, and real-world examples.
- A compact approach towards learning complex concepts of Data Mining.
- Step-by-step procedures to explain the techniques and algorithms.
Book #3: Data Mining Techniques Paperback
Author: Arun K Pujari
Get the book here.
Review:
The book updates the readers on modern data mining and warehousing techniques. It deals with association rules, clustering, neural networks, and genetic algorithms. The book covers temporal data and web data mining.
Key Takeaways from that book
- The book includes Java and Python programming examples for hands-on implementation and experimentation.
- Explores classification and clustering methods, offering explanations and practical examples for a deeper understanding.
- Covers association rule mining, which is vital for pattern discovery in large datasets, is commonly applied in retail analysis.
Book #4: Introduction to Data Mining
Author: Pang Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar
Get this book here.
Review:
Are you new to the concept of Data Mining? Well, this book is an essential guide to getting introduced to this concept. It is a compact volume, catering to the needs of professionals and students strategically. Some of the key areas that this book covers include association analysis, anomaly detection, predictive modeling, and cluster analysis.
Key Takeaways from that book
- Addresses text mining and web mining, highlighting their significance in analyzing unstructured data from the internet.
- Introduces ensemble learning methods, which combine multiple models to improve predictive accuracy.
- Emphasizes the importance of data preprocessing, covering data cleaning, transformation, and feature selection techniques.
- Provide hands-on exercises in Weka and R for practically applying data mining concepts.
Book #5: Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
Author: Jared Dean
Get the book here
Review:
Big data translates to big business. Business Leaders, techies, and marketing people learn to handle Big Data, Machine Learning, Data Mining, and Value Creation. It addresses the future trends in performance computing architectures.
Key Takeaways from that book
- The book covers computing architectures for analytics,
- The book discusses in-memory databases.
- It furthers my skills in data mining, text analytics, and machine learning algorithms.
Book #6: Data Mining Techniques
Author: Gordon S. Linoff, Michael J.A. Berry
Get the book here.
Review:
This book covers the latest techniques used in data mining and guides those trying to make a career in data mining. Each page is carefully written to solve business problems using the latest techniques and methodologies used in Data Mining.
Key Takeaways from that book
- The author sincerely attempts to identify the latest business problems and provides necessary solutions using different data mining methods.
- The reader identifies different credit risk factors and provides essential guidance toward managing the same.
- The book is compact and has many examples.
Book #7: Data Mining
Author: Ian. H. Witten, Mark. A. Hall, Cristopher. J. Pal, Eibe Frank.
Get the book here.
Review:
This book uses a practical approach to machine learning and data mining concepts. The best part about this book is the inclusion of machine learning algorithms that help clear several concepts. It is a perfect book for practitioners as well as for those who are working on Machine Language and Data Mining.
Key Takeaways from that book
- The book teaches data mining using Weka, open-source software, with practical, hands-on examples for concept illustration.
- Covers advanced topics like text mining and ensemble methods for diverse readers.
- Explains classification and clustering, their workings, and practical applications.
Book #8: Insight Into Data Mining: Theory and Practice
Author: K.P. Soman, Shyam Diwakar, V. Ajay
Get the book here.
Review:
The concept of data mining is making big rounds in industries since handling data in bigger volumes becomes easier using this approach. This book focuses on different data mining algorithms and methodologies to handle big volumes of data in warehouses. It is a compact volume that uses real-world examples to identify data management problems and makes necessary attempts to determine the best possible solutions. This book can prove effective for undergraduate students and those in marketing research and bioinformatics.
Key Takeaways from that book
- Authors blend theory and practical application for effective real-world data mining.
- Covers vital data visualization for insightful and effective result presentation.
- Emphasizes ethics, responsible data handling, and privacy in data mining.
Book #9: Introduction to Data Mining with Case Studies
Author: G.K. Gupta
Get the book here.
Review:
It is a very innovative book on data mining. The volume focuses on differing real-world case studies to explain the varied data mining concepts. The beginners or intermediates find the book meritorious with comprehensive information.
Key Takeaways from that book
- The book includes numerous case studies that give readers a deep understanding of the methodologies and problems.
- The book features several class projects for practice.
- The content provides explanatory notes on the latest data mining techniques and methodologies.
Book #10: Data Mining and Data Warehousing: Principles and Practical Techniques
Author: Parteek Bhatia.
Get the book here.
Review:
Very few books combine the concepts of data warehousing and mining in a well-formed way, unlike this volume. It’s written in simple language and covers essential topics like decision trees, distance metrics, data marts, information theory, partitioning clustering, and Naïve Bayes classifier, to mention a few. Each chapter includes several examples that help even more in clearing the concepts.
Key Takeaways from that book
- The book offers an in-depth discussion of Relational Data Models and Big Data Analytics.
- The book explains Cluster Analysis using different examples.
- The content features various MCQs and problems to test the readers’ skills.
Recommended Books
Our top 10 data mining books offer a blend of theoretical knowledge and practical application, ensuring that readers not only grasp the underlying principles but also learn how to implement data mining techniques effectively in real-world scenarios. They explore critical areas such as data preprocessing, classification, clustering, association rule mining, and ethical considerations, providing a well-rounded education in data mining. For more such books, EDUCBA recommends the following,
