Thinking-data-science-a-data-science-practitioners-guide

Preface
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Now comes the next challenge for a data scientist, and that is clustering a dataset
without having labeled data points. We call this unsupervised learning. I have a huge
section (Part II) comprising Chaps. 9 through 16 for clustering, giving you an indepth
coverage for several clustering techniques. The notion of cluster is not welldefined
and usually there is no consensus on the results produced by clustering
algorithms. So, we have lots of clustering algorithms that deal with small, medium,
large, and really huge spatial datasets. I cover many clustering algorithms explaining
their applications for various sized datasets.
Chapter 9 (Centroid-Based Clustering) discusses the centroid-based clustering
algorithms, which are probably the simplest and are the starting points for clustering
huge spatial datasets. The chapter covers both K-Means and K-Medoids clustering
algorithms. For K-Means, I describe its working followed by the explanation of the
algorithm itself. I discuss the purpose of objective function and techniques for
selecting optimal clusters. These are called Elbow, Average Silhouette, and the
Gap Statistic. This is followed by a discussion on K-Means limitations and where
to use it? For the K-Medoids algorithm, I follow a similar approach describing its
working, algorithm, merits, demerits, and implementation.
Chapter 10 (Connectivity-Based Clustering) describes two connectivity-based
clustering algorithms: Agglomerative and Divisive. For Agglomerative clustering,
I describe the Simple, Complete, and Average linkages while explaining its full
working. I then discuss its advantages, disadvantages, and practical situations where
this algorithm finds its use. For Divisive clustering, I take a similar approach and
discuss its implementation challenges.
Chapter 11 (Gaussian Mixture Model) describes another type of clustering
algorithm where the data distribution is of Gaussian type. I explain how to select
the optimal number of clusters with a practical example.
Chapter 12 (Density-Based Clustering) focuses on density-based clustering techniques.
Here I describe three algorithms—DBSCAN, OPTICS, and Mean Shift. I
discuss why we use DBSCAN? After discussing preliminaries, I discuss its full
working. I then discuss its advantages, disadvantages, and implementation with the
help of a project. To understand OPTICS, I first explain to you a few terms like core
distance and reachability distance. Like the earlier one, I discuss its implementation
with the help of a project. Finally, I describe Mean Shift clustering by explaining its
full working and how to select the bandwidth. A discussion on the algorithm’s
strengths, weaknesses, applications, and a practical implementation illustrated with
the help of a project follows this.
Chapter 13 (BIRCH) discusses another important clustering algorithm called
BIRCH. This is an algorithm that helps data scientists in clustering huge datasets,
where all the earlier algorithms fail. BIRCH splits the huge dataset into subclusters
by creating a hierarchical tree-like structure. The algorithm does clustering incrementally
eliminating the need for loading the entire dataset into memory. In this
chapter, I discuss why and where to use this algorithm and explain its working by
showing you how the algorithm constructs a CF tree.
Chapter 14 (CLARANS) discusses another important algorithm for clustering
enormous sized datasets. This is called CLARANS. The CLARA (Clustering for