Best Books For Learning Data Mining

Posted on 08-15-2017 by onlinebooksreview

Data mining is the 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. To learn about more data mining and how it works you should read this book.

1. Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (Morgan Kaufmann Series in Data Management Systems)

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, plus 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 algorithm
  • Implementing real machine learning schemes
  • Principles of data transformation
  • Numeric prediction with 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

2. Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

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

3. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

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 analysis 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 classic 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

4. Data Mining: The Textbook

Data Mining: The Textbook covers a wide range of data mining concepts from the fundamentals to the complex data types and their applications. With 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

5. Data Mining and Analysis: Fundamental Concepts and Algorithms

This is a solid guidance in data mining for students, researchers, and practitioner. 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

6. Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner

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 necessary knowledge of different data mining techniques and 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

7. Data Mining for the Masses, Second Edition: with implementations in RapidMiner and R

If you're looking for the 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

8. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!)

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 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