The necessity to handle many complex statistical analysis projects is hitting statisticians and analysts across the globe. Since there is an increasing interest in data analysis, R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers functionality to deal with a truly vast array of problems.
It's been decades since the R programming language was born, and it has become eminent and well known not only within the community of scientists but also in the wider community of developers. It has grown into a powerful tool to help developers produce efficient and consistent source code for data-related tasks. The R development team and independent contributors have created good documentation, so getting started with R programming isn't that hard.
To go further, you can use packages from the official R website. If you want to continually improve your level of expertise, you might read through a set of books that have been published in last couple of years. You should always bear in mind that creating high-level, secure, and internationally compliant code is more complex than the first application created in the beginning.
This book is designed to help you deal with an array of problems that you may encounter during complex statistical projects, which can be difficult. Topics in this book will include learning how to manipulate data with R using code snippets, mining frequent patterns, association, and correlations while working with R programs. This book will also provide for those with only a basic knowledge of R the skills and knowledge to successfully create and customize the most popular data mining algorithms. This will help overcome difficulties encountered and will ensure the most effective use of the R programming language on data mining algorithm development through its rich set of publicly available packages.
Each chapter of this book is intended to stand on its own, so feel free to jump to any chapter where you feel you need to get more in-depth knowledge about a particular topic. If you feel you missed something major, go back and read the earlier chapters. They are constructed in a way to grow your knowledge piece by piece.
Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions based on the MapReduce algorithm. You will finish this book feeling confident in the ability that you know which data mining algorithm to apply in which situation.
I enjoy working with the R programming language for versatile data mining tasks developments and researches, and I am really happy to share my enthusiasm and expertise with you to help you make use of the language more effectively and comfortably use data mining algorithm developments and applications.
Chapter 1, Warming Up, gives you the overview of data mining, the relation of data mining to machine learning, and statistics. It illustrates basic data mining terms such as data definition and preprocessing.
Chapter 2, Mining Frequent Patterns, Associations, and Correlations, contains advanced and interesting algorithms required to learn mining frequent patterns, association rules, and correlation rules when working with R programs.
Chapter 3, Classification, helps you learn the classic classification algorithms written in the R language, covering various classification algorithms for different types of datasets.
Chapter 4, Advanced Classification, teaches you more classification algorithms, such as the Bayesian Belief Network, SVM, and k-Nearest Neighbors algorithm.
Chapter 5, Cluster Analysis, helps you learn how to implement the popular and classic algorithms for clustering, such as k-means, CLARA, and spectral algorithms.
Chapter 6, Advanced Cluster Analysis, shows the implementation of advanced algorithms for clustering that are related to hot topics in current industries, including EM, CLIQUE, DBSCAN, and so on.
Chapter 7, Outlier Detection, demonstrates the classic and popular algorithms used to detect outliers in real-world cases.
Chapter 8, Mining Stream, Time-series, and Sequence Data, explains these three hot topics with the most popular, classic, and top-ranking algorithms.
Chapter 9, Graph Mining and Network Analysis, shows you the overview of graphs and social mining algorithms, along with other interesting topics.
Chapter 10, Mining Text and Web Data, helps you learn the popular algorithms applied in domains with interesting applications.
Appendix, Algorithms and Data Structures, contains a list of algorithms and data structures to help you on your data mining journey.