Currently, the amount of information we are able to produce is increasing exponentially. In the past, data storage was very expensive. However, today, new technologies make it cheaper to store this information. So we are able to generate massive amounts of data, which it is also feasible to store. This means that we are immersed in a universe of data, of which we are not able to exploit the vast majority.
Among these large deposits of data storage there is valuable knowledge, but it is hidden and difficult to identify using traditional methods.
Fortunately, new technologies such as artificial intelligence, machine learning, and the management of databases converge with other disciplines that are more traditional such as statistics or mathematics to create the means to locate, extract, or even construct this valuable information from raw data.
This convergence of knowledge areas gives rise to, for example, very important subfields such as supervised learning and unsupervised learning, both derived from machine learning.
Both subfields contain a large quantity of tools to enhance the use of stored data so that it is possible to generate knowledge about the data and extract it in a human-interpretable way.
In this book, you will learn how to implement some of the most important concepts of unsupervised learning directly in the R console, one of the best tools for a data scientist, through practical examples using more than 40 R packages and a lot of useful functions.
Considering the wide range of techniques and knowledge related to unsupervised learning, this book is not intended to be in any way exhaustive. However, it contains some valuable knowledge and main techniques to introduce the reader to the study and implementation of this important sub field of machine learning.
Chapter 1, Welcome to the Age of Information Technology, aims at introducing the reader to the unsupervised learning context and explains the relation between unsupervised and supervised learning in the context of data mining. It also provides the reader with an introduction to the key concepts of information theory.
Chapter 2, Working with Data – Exploratory Data Analysis, is about some techniques for exploratory data analysis such as summarization, manipulation, correlation, and data visualization. An adequate knowledge of data, by exploration, is essential in order to apply unsupervised learning algorithms correctly. This assertion is true for any effort in data mining, not just for unsupervised learning.
Chapter 3, Identifying and Understanding Groups – Clustering Algorithms, teaches the readers about one of the most used techniques in unsupervised learning, clustering. Identifying groups can help discover and explain some patterns hidden in data. It is frequently the answer for multiple problems in many industries or contexts. Finding clusters can help uncover relationships in data, which can in turn be used to support future decisions.
Chapter 4, Association Rules, covers another grouping technique, the association rules. The association process makes groups of observations and attempts to discover links or associations between different attributes of groups. This association becomes rules, which can in turn be used to support future decisions.
Chapter 5, Dimensionality Reduction, aims to explain some dimensionality reduction techniques. In machine learning, this concept is the process of reducing the number of random variables considered, and it can be subdivided into feature selection and extraction. The key is to reduce the number of dimensions, but preserve most parts of the information.
Chapter 6, Feature Selection Methods, explains some techniques for feature selection, also known as variable selection or attribute selection. The key point is to choose a subset of relevant features of variables for modeling and not to use features that seem to be redundant, considering correlation to simplify model construction.
Appendix, References, provides a list of links referenced in the book, which are sorted chapter-wise. Given the amount of package and functions used in this book, it is very difficult to cite references and authors within the text of each chapter, as it would appear intermittent for the reader.
You need to download R to follow the examples. You can download and install R using the CRAN website available at http://cran.r-project.org/. All the code was written using RStudio. RStudio is an integrated development environment (IDE) for R and can be downloaded from http://www.rstudio.com/products/rstudio/. Many of the examples are created using R packages, and they are discussed in their respective sections.
This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows:
A block of code is set as follows:
# Clean the Work Space
rm(list = ls(all = TRUE))
# Read the iris.csv file
Iris <- read.table("iris.csv", header = TRUE,
sep = ",",dec = ".", row.names = 1)In R it is a general practice to use <- for assignment instead of the = sign.
New terms and important words are shown in bold. Words that you see on the screen, for example in menus or dialog boxes, appear in the text like this: "We can also use the Summary of dataset option for exploratory data analysis:"
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