Learning Data Mining with Python

Discovering Accounts to Follow Using Graph Mining
The aim of this analysis was to recommend users, and our use of cluster
analysis allowed us to find clusters of similar users. To do this, we found
connected components on a weighted graph we created based on this similarity
metric. We used the NetworkX package for creating graphs, using our graphs,
and finding these connected components.
We then used the Silhouette Coefficient, which is a metric that evaluates how good
a clustering solution is. Higher scores indicate a better clustering, according to the
concepts of intra-cluster and inter-cluster distance. SciPy's optimize module was
used to find the solution that maximises this value.
In this chapter, we compared a few opposites too. Similarity is a measure
between two objects, where higher values indicate more similarity between those
objects. In contrast, distance is a measure where lower values indicate more
similarity. Another contrast we saw was a loss function, where lower scores are
considered better (that is, we lost less). Its opposite is the score function, where
higher scores are considered better.
In the next chapter, we will see how to extract features from another new type of
data: images. We will discuss how to use neural networks to identify numbers in
images and develop a program to automatically beat CAPTCHA images.
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