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Looking at clustering similarity metrics
Classification methods are based on calculating the similarity or difference between two
observations. If your dataset is numeric — composed of only numerical features — and can be
portrayed on an n-dimensional plot, you can use various geometric metrics to scale your
multidimensional data.
An n-dimensional plot is a multidimensional scatter plot that you can use to plot n number
of dimensions of data.
Some popular geometric metrics used for calculating distances between observations are simply
different geometric functions that are useful for modeling distances between points:
Euclidean metric: A measure of the distance between points plotted on a Euclidean plane.
Manhattan metric: A measure of the distance between points where distance is calculated as
the sum of the absolute value of the differences between two points’ Cartesian coordinates.
Minkowski distance metric: A generalization of the Euclidean and Manhattan distance
metrics. Quite often, these metrics can be used interchangeably.
Cosine similarity metric: The cosine metric measures the similarity of two data points based
on their orientation, as determined by taking the cosine of the angle between them.
Lastly, for non-numeric data, you can use metrics like the Jaccard distance metric, an index that
compares the number of features that two observations have in common. For example, to illustrate
a Jaccard distance, look at these two text strings:
Saint Louis de Ha-ha, Quebec
St-Louis de Ha!Ha!, QC
What features do these text strings have in common? And what features are different between
them? The Jaccard metric generates a numerical index value that quantifies the similarity between
text strings.
Identifying Clusters in Your Data
You can use many different algorithms for clustering, but the speed and robustness of the k-means
algorithm makes it a popular choice among experienced data scientists. As alternatives, kernel
density estimation methods, hierarchical algorithms, and neighborhood algorithms are also
available to help you identify clusters in your dataset.
Clustering with the k-means algorithm
The k-means clustering algorithm is a simple, fast, unsupervised learning algorithm that you can
use to predict groupings within a dataset. The model makes its prediction based on the number of