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Unsupervised Learning
• Inspector Panel: On the right-hand side, here you can search for particular
points and see a list of nearest neighbors.
Figure 2: Screenshow of the Embedding Projector tool
K-means clustering
K-means clustering, as the name suggests, is a technique to cluster data, that is, to
partition data into a specified number of data points. It is an unsupervised learning
technique. It works by identifying patterns in the given data. Remember the sorting
hat of Harry Potter fame? What it is doing in the book is clustering—dividing new
(unlabeled) students into four different clusters: Gryffindor, Ravenclaw, Hufflepuff,
and Slytherin.
Humans are very good at grouping objects together; clustering algorithms try
to give a similar capability to computers. There are many clustering techniques
available, such as Hierarchical, Bayesian, or Partitional. K-means clustering belongs
to partitional clustering; it partitions the data into k clusters. Each cluster has a
center, called the centroid. The number of clusters k has to be specified by the user.
The k-means algorithm works in the following manner:
1. Randomly choose k data points as the initial centroids (cluster centers)
2. Assign each data point to the closest centroid; there can be different measures
to find closeness, the most common being the Euclidean distance
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