Best Data Mining Books in 2022 | Master the Techniques of Data Mining
Data mining is the practice of examining large databases in order to generate new information. It is one kind of process. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs.
In this article, we reviewed some of the best data mining books in 2022. Look at them carefully and pick the perfect ones for you.
You'll learn how to:
- Think statistically and understand the role variation plays in your life and decision making
- Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace
- Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence
- Avoid common pitfalls when working with and interpreting data
Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.
What You Will Learn
- Comprehend data mining using a visual step-by-step approach
- Build on a theoretical introduction of a data mining method, followed by an Excel implementation
- Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone
- Become skilled in creative uses of Excel formulas and functions
- Obtain hands-on experience with data mining and Excel
This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.
Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation. With a dual focus on concepts and operations, this text comprises a complete how-to and is an excellent resource for anyone considering the field. Case studies and hands-on activities incorporate real-world data sets and allow students the opportunity to exercise their new skills. Our Cloud Desktop integrates popular data mining tools, giving students a valuable familiarity with industry-standard applications. After defining the concepts of data mining and machine learning, Data Mining and Analytics delves into the types of databases, their respective relevance and popularity, and the trends that affect their use.
The importance of data visualization for communication purposes is explored, as are the processes of data cleansing, clustering, and classification. Excel, SQL, NoSQL, Python, and R programming all receive in-depth treatments, supplemented with hands-on exercises. Operations covered in earlier chapters are given real-world context through a practical application to the current issues of “big data” and of text and image data mining. The text concludes by describing an analyst’s steps from planning through execution, ensuring that readers gain the technical know-how to launch, lead, or support a data project in the workplace.
This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:
- A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
- A new section on ethical issues in data mining
- Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
- More than a dozen case studies demonstrating applications for the data mining techniques described
- End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
- A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions
Using Microsoft Office Exel add-in XLMiner, Data Mining for Business Intelligence guides you through developing predictive models and techniques for describing and finding patterns in your business data. It supplies insightful, detailed guidance on fundamental data mining techniques. With interesting and real-world examples, you'll be able to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization.
What you'll learn:
- A complete overview of the data mining process
- Techniques for effective data visualization
- Basic principles of dimension reduction
- Evaluating classification and predictive performance
- Multiple linear regression and logistic regression
- Prediction and classification methods such as Naive Bayes, K-nearest neighbors and neural nets
- Discriminant analysis and cluster analysis
- Regression-based forecasting and smoothing methods
This leading introductory book on data mining shows you how to harness the newest data mining methods and techniques to solve your common business problems. In this book, you'll find invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. You'll also understand the advanced topics of data mining such as preparing data for analyzing and creating the necessary infrastructure for data mining at your organization.
What you'll learn:
- Applications of data mining in marketing and customer relationship management
- The data mining process
- Profiling and predictive modeling
- Data mining using classical statistical techniques
- Working with artificial neural networks
- Using survival analysis to understand customers
- Genetic algorithms and swarm intelligence
- Automatic cluster detection
- Market basket analysis and association rules
- Data warehousing, OLAP, analytic sandboxes and data mining
- Techniques for reducing the number of variables
Data Mining: The Textbook covers a wide range of data mining concepts from the fundamentals to the complex data types and their applications. By explaining traditional data mining problems, it also introduces advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. It is appropriate for both introductory and advanced data mining courses for the students of this field. If you're an industrial practitioner you can use it as a reference book as well.
What you'll learn:
- Data preparation process including data cleaning, data reduction, and transformation
- Finding similarities and distances in your data
- Basic and advanced strategies for association pattern matching
- Different cluster analysis techniques
- Methods for outliers analysis
- Data classification using rule-based classifiers, probabilistic classifiers, neural networks, and support vector machines
- Semisupervised learning and ensemble methods
- Mining data streams and text data
- Social network analysis
- Mining web data and graph data
- Privacy-preserving data mining concepts
This is solid guidance in data mining for students, researchers, and practitioners. It covers all the core methods and cutting-edge research in this field. With real-world examples, it provides an in-depth overview of data mining and integrates related concepts from machine learning and statistics. Reading this book, you'll find learning data mining fun and easy.
What You'll learn:
- Different attributes such as numeric and categorical attributes
- How to represent your data using graph
- Kernal methods for data mining
- Mining frequent pattern in your data
- Representative-based and density-based clustering
- Spectral and graph clustering
- Probabilistic classification and decision tree classifiers
- Linear decrement analysis
- Working with support vector machines
This is a useful data mining book for all, whether you're a novice data miner or doing data mining projects for your company. With this book, you can understand how to analyze data and uncover the hidden patterns and relationships to make important decisions and predictions. You'll gain the necessary knowledge of different data mining techniques and the simple step-by-step process for predicting an outcome or discovering hidden relationships in your data. Along with that, this book introduces RapidMiner, an open-source GUI based data mining tool that will help you do effective data mining operations.
What you'll learn:
- Exploratory data analysis and visualization
- Different data mining algorithms including k-Nearest Neighbors and Naïve Bayesian
- K-Means clustering and Density-based clustering
- Artificial Neural Networks and Support Vector Machines
- Association analysis using Apriori and FP Growth
- Regression analysis using Linear regression and Logistic regression methods
- Building self-organizing maps
- Mining text and web data
- Time-series forecasting, Anomaly detection, and Feature selection
If you're looking for a guide that could explain data mining in a simple, concise and effective way, check this book. In this book, you'll find simple examples and clear explanations with free, powerful software tools to teach yourself the basics of data mining. Implementations of these examples are offered in both an updated version of the RapidMiner software, and in the popular R Statistical Package.
What you'll learn:
- Basics of data mining
- Organizational understanding and data understanding
- Applying the CRISP data mining model
- Correlation methods
- Association rules
- Discriminant analysis, k-nearest neighbors, and Naive Bayes
- Linear and logistic regression in R
- Decision trees and neural networks
- Data mining ethics
This book follows a simple and easy-to-understand approach to teach you the basic aspects of data mining with Rattle and R. Data Mining with Rattle and R encourages the concept of programming through example and programming with data - more than just pushing data through tools. You can still read this book without having any prior experience with data mining or programming. With this book, you'll have a clear understanding of data mining and its usage.
What you'll learn:
- Your first data mining project using Rattle and R
- Data nomenclature and sourcing data for mining
- Summarizing and visualizing the dataset
- Interactive graphics including Latticist and GGobi
- Transforming data
- Descriptive and predictive analytics
- Cluster and association analysis
- Decision trees and random forests
- Model performance evaluation
- Deploying your R model
This book covers a detailed overview of applying machine learning tools and techniques in real-world data mining situations. With this book, you'll know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods to complete successful data mining. In this updated edition, you'll find modern concepts of Data Mining such as Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, a new version of the popular Weka machine learning software developed by the authors.
What you'll learn
- Fundamentals of concepts, instances, and attributes
- The basic methods of designing the algorithm
- Implementing real machine learning schemes
- Principles of data transformation
- Numeric prediction with the local linear model
- Bayesian network and clustering
- Semi-supervised and multi-instance learning
- Ensemble learning including bagging, randomization and boosting
- Learning from massive data sets
- Incorporating domain knowledge
- Web mining and text mining
- Using the WEKA data mining tool
This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.