30 Best Books on Data Mining

We have compiled a list of the Best Reference Books on Data Mining, which are used by students of top universities, and colleges. This will help you choose the right book depending on if you are a beginner or an expert. Here is the complete list of Data Mining Books with their authors, publishers, and an unbiased review of them as well as links to the Amazon website to directly purchase them. If permissible, you can also download the free PDF books on Data Mining below.

1. Data Mining and Data Warehousing

 
1."Data Mining: Concepts and Techniques" by Han
“Data Mining: Concepts and Techniques” Book Review: This book is a valuable resource for those seeking to comprehend and employ the theory and application of uncovering patterns concealed within vast data sets. It highlights crucial emerging topics within the field, such as data warehouses and data cube technology, as well as the extraction of insights from social networks, streaming data, and intricate data types like spatial and multimedia data. The text delves into sophisticated areas like mining object-relational databases, multimedia databases, time-series databases, and text databases. It offers a comprehensive and practical understanding of the methods and concepts involved. Overall, the book is suitable for anyone seeking to acquire potent data mining techniques to overcome actual business problems.

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2."Data Warehousing" by Reema Thareja
“Data Warehousing” Book Review: This book offers a comprehensive overview of data warehousing fundamentals, equipping readers with the necessary knowledge to create and manage their own data warehouse. It covers the various features and architecture of data warehouses, followed by a detailed exploration of business requirements and dimensional modeling. Additionally, it delves into the components of a data warehouse and provides a thorough understanding of the building and maintenance processes. To aid comprehension, the book provides numerous examples and review questions, as well as other end-of-chapter exercises. It caters to students of Computer Science & Engineering (BE/Btech), computer applications (BCA/MCA), and computer science (B.Sc).

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3."Data Warehousing and Data Mining" by Singh M
“Data Warehousing and Data Mining” Book Review: This book is a comprehensive guide that delves into the fundamental concepts of data mining and warehousing, providing an in-depth understanding of the subject matter to both undergraduate and graduate students of computer science. It features a multitude of case studies from industry experts, which offer practical insights into the field. The book also provides detailed explanations of practical applications with software tools like Oracle BI, Weka and R. It covers the latest research trends in data mining, including topics such as ensemble learning, Web mining, bioinformatics and data warehousing with Oracle BI. Moreover, it explores emerging areas such as spatial data mining, big data, cloud computing and CRM. This book is an indispensable resource for students who wish to gain a comprehensive understanding of data mining and warehousing.

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4."Data Mining and Warehousing" by S Prabhu
“Data Mining and Warehousing” Book Review: This comprehensive guide offers a methodical overview of the fundamental principles of Data Mining and Data Warehousing. It encompasses an extensive array of data mining algorithms such as prediction, classification, and association, along with their applications and related products. The book meticulously details the implementation stages of these processes, including ETL tools, software products, schemas, partition, backup, recovery, and tuning. It caters to the needs of undergraduate and postgraduate students pursuing Computer Science and Information Technology, as well as practitioners and research scholars seeking relevant insights.

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5."Data Mining and Warehousing" by Khushboo and Sandeep
“Data Mining and Warehousing” Book Review: This book presents a thorough collection of research findings in a rapidly growing and crucial domain. It equips readers with a range of resources, frameworks, and results pertaining to the employment of data mining and warehousing technologies, including algorithms, concept lattices, multidimensional data, and online analytical processing. The book presents a broad survey of methodologies, strategies, unsolved issues, and use cases related to data warehousing and mining. It is intended for students pursuing Computer Science & Engineering (BE/Btech), computer applications (BCA/MCA), and computer science (B.Sc) degrees.

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6."Introducing Data Science: Big Data, Machine Learning, and more, using Python tools" by Davy Cielen and Arno Meysman
“Introducing Data Science: Big Data, Machine Learning, and more, using Python tools” Book Review: This book provides an in-depth understanding of crucial data science concepts and equips readers with the necessary skills to undertake fundamental tasks that form the core of data scientists’ roles. Key topics such as data visualization, graph databases, NoSQL usage, and the data science process are comprehensively covered. Readers will gain insights into how Python can be used to analyze big data sets that require storage across multiple machines. The book caters to the needs of Computer Science and Information Technology students at both undergraduate and postgraduate levels.

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7."Data Warehousing: OLAP and Data Mining" by Nagabhushana S
“Data Warehousing: OLAP and Data Mining” Book Review: The book encompasses an extensive range of topics related to data warehousing, including planning, managing, designing, implementing, supporting, maintaining, and analyzing data warehouses within organizations. It delves into various mining techniques and explores practical issues that arise when using Data Mining Tools. With its target audience being IT students and professionals, the book serves as an excellent resource for learning and implementing data warehousing technologies. Additionally, specialists, trainers, and IT users can also benefit from the valuable insights presented in the book.

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8."Data Mining and Warehousing" by M Sudheep Elayidom
“Data Mining and Warehousing” Book Review: This book is a comprehensive guide to the fundamental principles of data mining and warehousing. It features a range of case studies from industry experts, providing students with a practical understanding of the subject matter. The book delves into the practical aspects of data mining, complete with detailed explanations and software tools like Oracle BI, Weka, and R. It covers the latest research trends in the field, including ensemble learning, Web mining, bioinformatics, and data warehousing with Oracle BI. Additionally, it explores spatial data mining, big data, cloud computing, and CRM. The book is an ideal resource for both graduate and undergraduate students studying computer science.

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9."Python for Data Science for Dummies" by John Paul Mueller and Luca Massaron
“Python for Data Science for Dummies” Book Review: The book is a comprehensive guide to Python programming, which demonstrates the benefits of using Python for acquiring, organizing, processing, and analyzing vast amounts of data to detect trends and patterns. It presents fundamental concepts in Python and covers the essentials of Python data analysis programming and statistics. Additionally, the book delves into crucial data science concepts, such as probability, hypothesis testing, regression models, and random distributions. Readers will acquire knowledge of statistical concepts, including probability and random distributions, and develop proficiency in their application. The book is a valuable resource for data scientists and students interested in learning about Python and its practical applications.

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10."The Encyclopedia of Data Warehousing and Mining" by John Wang
“The Encyclopedia of Data Warehousing and Mining” Book Review: This publication presents a detailed exploration of data warehousing and mining, offering a comprehensive analysis of the significant topics in this fast-evolving field. It offers a broad overview of ETL tools, software products, schemas, partitioning, backup, recovery, and tuning, providing readers with a holistic understanding. This resource will prove beneficial for both undergraduate and postgraduate students of Computer Science and Information Technology, as well as practitioners and research scholars.

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11."DATA MINING AND ANALYSIS:FUNDAMENTAL CONCEPTS AND ALGORITHMS" by MOHAMMED J ZAKI/ WAGNER MEIRA Jr
“DATA MINING AND ANALYSIS:FUNDAMENTAL CONCEPTS AND ALGORITHMS” Book Review: This book provides an introduction to data mining and analysis algorithms, starting from the fundamentals. It covers techniques for examining patterns and models in various types of data, with practical applications in fields ranging from scientific research to business intelligence and analytics. Designed as a textbook for undergraduate and graduate students in the field of data mining, it covers topics such as exploratory data analysis, pattern mining, clustering, and classification, supplemented with numerous algorithmic examples.

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12."Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining)" by Daniel T Larose and Chantal D Larose
“Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining)” Book Review: This book is intended for students in the fields of Data Mining, Predictive Analytics, Computer Science, Statistics, MBA, and Chief Executive Studies. It covers various topics such as association rules, clustering, neural networks, logistic regression, and multivariate analysis, as well as hands-on analysis problems. Additionally, the book includes problems and solutions based on large, real-world datasets.

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13."Advances in Machine Learning and Data Mining for Astronomy (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)" by Michael J Way
Advances in Machine Learning and Data Mining for Astronomy (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)” by Michael J Way Book Review: This book offers valuable insights into the process of improving the quality of various products by providing a better understanding of consumer needs, current product and process performance, and optimal future states. In 2009, Frank Rossi and Viktor Mirtchev developed the course “Statistics for Food Scientists” to assist product and process engineers in increasing their project success rates through the integration of practical statistical thinking in their work. This book is based on the course and presents detailed descriptions of statistical concepts and commonly used tools, comprehensive examples and specific practical problems that food scientists face in their work, and how statistical tools can help them make more informed decisions. It provides information on how statistical tools are applied to improve research results, enhance product quality, and promote overall product development.

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14."Optimization Based Data Mining" by Yingjie Tian
“Optimization Based Data Mining” Book Review: This book offers a superb mix of theoretical and practical concepts in data mining. It primarily focuses on support vector machines and multiple criteria programming, as evidenced by its chapters. The authors provide readers with relatable content and practical knowledge by showcasing numerous case studies and real-life applications of data mining. The book also provides insights into potential areas for further research and informs readers about the latest developments in this field. Practitioners and graduates working in data mining, bioinformatics, and petroleum engineering will find this book to be a valuable asset.

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15."Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)" by Charu C Aggarwal and Chandan K Reddy
“Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)” Book Review: This book comprehensively covers topics related to data clustering approaches, ranging from basic to more complex concepts. It is organized into three primary aspects, namely Methods, Domains, and Variations and Insights, each of which is explored in depth. The Methods aspect covers a wide range of clustering techniques, such as feature selection, agglomerative, partitional, density-based, probabilistic clustering, and more. The Domains aspect delves into various types of data, including categorical, text, multimedia, graph, biological, stream, and others. Lastly, the Variations and Insights aspect discusses advanced topics like semi-supervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation. The book also provides insights into techniques for verifying the quality of clusters through supervision, human intervention, and the automated generation of alternative clusters. It is mainly intended for researchers and practitioners in the field of Data Clustering Algorithms and Applications.

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16."Pocket Data Mining: Big Data on Small Devices (Studies in Big Data)" by Mohamed Medhat Gaber and Frederic Stahl
“Pocket Data Mining: Big Data on Small Devices (Studies in Big Data)” Book Review: This book introduces the Pocket Data Mining (PDM) project, which takes advantage of the seamless communication among handheld devices to perform data analysis tasks that were previously impossible. PDM involves the collaborative extraction of knowledge from distributed data streams in a mobile computing environment. The book offers an in-depth treatment of this emerging area of research, including detailed descriptions of techniques used and thorough experimental studies. It also provides a practical guide to deploying PDM in a mobile environment, as well as an extension to deal with concept drift. The book explores the potential applications of PDM in a variety of domains, including security, business, and telemedicine, making it an important read in the era of Big Data.

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17."Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)" by Alexander Paprotny and Michael Thess
“Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)” Book Review: This book showcases promising results from various experiments conducted on real-world data. The field of real-time data mining is advancing rapidly, and real-time data mining systems are the modern equivalent of “classic” data mining systems. The book offers a comprehensive overview of real-time analytics methods and highlights their pros and cons in comparison to traditional analytics methods, which solely rely on historical data. The book emphasizes the challenges in developing theoretically sound real-time analytics methods. Additionally, it incorporates application-oriented mathematicians by merging some of the most promising mathematical fields such as control theory, multilevel approximation, and tensor factorization.

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18."Data Mining: Uses in Commercial Applications" by Katiyar Vinodani
“Data Mining: Uses in Commercial Applications” Book Review: This book explores the potential of neural networks in data mining, an approach that has been successful in many situations but is still not widely used by practitioners. The book covers a range of topics, including (1) implementing neural networks to analyze marketing variables, (2) using sensitivity analysis and weight analysis to reduce variables, and (3) demonstrating how including unknown factors can lead to better results from neural networks. With the rise of industry-driven research, neural network techniques are becoming increasingly relevant as businesses tackle new challenges in data analysis.

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19."Data Mining and Business Analytics with R" by Johannes Ledolter
“Data Mining and Business Analytics with R” Book Review: This book serves as a valuable resource for those seeking guidance on modeling and interpreting complex data, as well as developing proficient skills in constructing robust models for prediction and classification. Additionally, it offers a comprehensive examination and practical demonstrations of the underlying principles of the most effective data mining tools. Real-world examples and readily available data sets with related R code are included, enabling readers to apply the outlined concepts to their own analyses. The book also features numerous exercises to reinforce computing skills and deepen comprehension of the material. It is particularly beneficial for professionals involved in data collection and analysis within finance, operations management, marketing, and the information sciences.

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2. Sequence Analysis and Data Mining

 
1."Data Mining Techniques for Protein Sequence Analysis" by Duraisamy Ramyachitra and Pandurangan Manikandan
“Data Mining Techniques for Protein Sequence Analysis” Book Review: This book covers a range of topics related to bioinformatics, datamining, and evolutionary computing, with a particular focus on their intersections. The text explores the application of data-mining techniques, using selected evolutionary approaches to examine protein structure, segmentation, and the current state of the field. Additionally, the book provides an introduction to the fundamental principles and terminology of evolutionary algorithms, highlighting two specific algorithms: differential evolution and self-organizing migrating algorithm.

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2."Sequence Data Mining (Advances in Database Systems)" by Guozhu Dong and Jian Pei
“Sequence Data Mining (Advances in Database Systems)” Book Review: This book covers two important topics in data mining: frequent/closed sequence patterns and similarity sequence patterns and motifs. The author provides a well-balanced overview of existing results and mining methods for different pattern types. While there are numerous books available on data mining and sequence data analysis, none of them address both topics equally. Sequence Data Mining bridges this gap by presenting cutting-edge results and techniques in a single comprehensive volume. This book is ideal for professionals working in diverse fields such as bioinformatics, genomics, web services, and financial data analysis. Additionally, advanced-level students in computer science and bioengineering will find it a valuable resource.

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3."Problems and Solutions in Biological Sequence Analysis" by Mark Borodovsky and Svetlana Ekisheva
“Problems and Solutions in Biological Sequence Analysis” Book Review: This book offers an extensive compilation of bioinformatics problems, accompanied by solutions. Notably, the problem set covers all the exercises provided in the widely-adopted textbook Biological Sequence Analysis by Durbin et al. (Cambridge, 1998), which is a required reading for bioinformatics courses at leading universities around the world. Although many of the problems in Biological Sequence Analysis have been commonly used for assignments and examinations, no detailed solutions were available, which led to a persistent demand from bioinformatics instructors for comprehensive worked solutions and a broader range of problems for instructional use. This book addresses that need by presenting a similar structure to Biological Sequence Analysis while significantly expanding the number of solvable problems. It is expected to facilitate better comprehension of the contents of the textbook and to promote the development of problem-solving skills crucial for successful research in the dynamic field of bioinformatics.

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4."Advances in Knowledge Discovery and Data Mining" by Joshua Zhexue Huang and Longbing Cao
“Advances in Knowledge Discovery and Data Mining” Book Review: The 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China, May 2007, is the source of the refereed proceedings presented in this book. The book features a comprehensive coverage of novel concepts, research outcomes, and practical experiences in all fields of KDD, such as data mining, machine learning, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition, and knowledge-based systems.

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5."Advances in Sequence Analysis: Theory, Method, Applications (Life Course Research and Social Policies)" by Philippe Blanchard and Felix Bühlmann
“Advances in Sequence Analysis: Theory, Method, Applications (Life Course Research and Social Policies)” Book Review: This book presents groundbreaking contributions to life course studies, examining various aspects of employment transitions, historical and contemporary careers, and political trajectories. The life-course perspective is central to the approach taken in this book, as well as the study of social processes more broadly. The volume aims to facilitate a dialogue between different traditions in sequence analysis that have developed independently across different disciplines and geographic regions. It provides updates on the latest developments in sequential concepts, coding, atypical datasets and time patterns, optimal matching and alternative algorithms, survey optimization, and visualization. The book not only reconsiders traditional uses of sequences but also promotes novel ways of collecting, formatting, representing, and processing them. The introduction covers fundamental sequential concepts and tools and presents a brief history of the method. Each chapter is designed to be both accessible to beginners and informative for experts.

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6."Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)" by Jiawei Han
“Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)” Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)” Book Review: This book provides an introduction to the essential principles of data mining and examines cutting-edge tools and methods. It clarifies elementary data mining concepts, such as OLAP, concept description, data preprocessing, classification and prediction, association rules, and cluster analysis. Moreover, it presents advanced data mining methods, including the extraction of data from diverse and intricate sources beyond relational databases, such as multimedia databases, object databases, time-series databases, and spatial databases. Additionally, the book explores the collection of data from various sources on the internet and extracting meaningful information from it. This resource is beneficial for computer science undergraduate and graduate students.

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3. Statistical Techniques in Data Mining

 
1."Principles of Data Mining" by D J Hand and P Smith
“Principles of Data Mining” Book Review: This book delves into the fundamental principles of data mining, encompassing an array of key topics such as data measurement, visualization and exploration, data analysis, and uncertainty. Additionally, readers can expect to gain insights on other pertinent subjects including an exhaustive survey of data mining algorithms, models, and patterns, as well as search and optimization techniques, and descriptive modelling. To facilitate ease of access, the book provides comprehensive lists of tables and figures at the onset. Furthermore, the algorithms and programs are meticulously discussed in detail, making this book an invaluable resource for undergraduates pursuing computer science and information technology studies.

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2."The Elements of Statistical Learning: Data Mining, Inference and Prediction" by T Hastie and J H Friedman
“The Elements of Statistical Learning: Data Mining, Inference and Prediction” Book Review: This book provides an introduction to the fundamental principles of statistical learning. It covers various main topics, including an overview of supervised learning, linear methods for regression, and linear methods for classification. In addition, the book delves into other important topics such as basic expansions and regularization kernel methods, as well as model assessment and selection. Furthermore, each chapter includes a set of exercises designed to help students practice and apply the concepts learned. This book is highly recommended for students studying computer engineering.

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3."Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions" by Nuno M.M. Ramos and João M.P.Q. Delgado
“Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions” Book Review: This book is aimed at advanced computer science engineering students and delves into the data mining techniques used to analyze indoor hygrothermal conditions. The main topics covered are indoor hygrothermal conditions, descriptive statistics, multiple regression analysis, and classification trees. In addition to these, the book also includes case studies on outdoor conditions, temperature, indoor relative humidity, and the application of data mining techniques. Each technique and method is described in detail, along with proper programs.

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4."Mastering Data Mining-The Art and Science of CRM" by M J A Berry and G S Linoff
Book Review: This book presents a case study that showcases the best practices in commercial data mining. It not only provides an overview of data mining tools and techniques, but also demonstrates how they can be used to make informed business decisions. The book emphasizes the importance of customer relationship management by shifting the focus from mere understanding of data mining techniques to achieving business results. Additionally, it applies data mining techniques to solve various practical business problems. The book also covers topics such as the formulation of business problems, data analysis, result evaluation, and more.

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5."Foundations of Predictive Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)" by James Wu and Stephen Coggeshall
“Foundations of Predictive Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)” Book Review: This book covers the basic principles of data analysis and model building for practical applications, including the statistical and linear algebra foundations of modeling methods. The content includes discussions on copula functions, Cornish-Fisher expansion, and other statistical techniques, as well as coverage of both linear and non-linear methods. Additionally, the book explores various methods used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. With its comprehensive coverage of data analysis and modeling techniques, this book provides readers with a wide range of tools for effectively analyzing and modeling data.

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We have put a lot of effort into researching the best books on Data Mining and came out with a recommended list and their reviews. If any more book needs to be added to this list, please email us. We are working on free pdf downloads for books on Data Mining and will publish the download link here. Fill out this Data Mining books pdf download" request form for download notification.

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