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cluster-analysis

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An introduction to Cluster Analysis and the K-Means clustering technique.

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# Cluster Analysis

Cluster analysis plays an important role in exploratory data analysis, data preprocessing, and unsupervised learning tasks. Here, we'll find two notebooks: [Cluster Analysis.ipynb](https://github.com/alexandrehsd/Cluster-Analysis/blob/master/Cluster%20Analysis.ipynb) and [Silhouette Analysis.ipynb](https://github.com/alexandrehsd/Cluster-Analysis/blob/master/Silhouette%20Analysis.ipynb)

[Cluster Analysis.ipynb](https://github.com/alexandrehsd/Cluster-Analysis/blob/master/Cluster%20Analysis.ipynb) addresses questions like:

1. How to perform cluster analysis using the K-Means technique?
  2. How to find the optimal number of clusters?
  3. How to identify appropriate features?
  4. Why and when do we need standardize the data?
  5. Which are the pros and cons of using K-Means?
  6. How to interpret the results?

[Silhouette Analysis.ipynb](https://github.com/alexandrehsd/Cluster-Analysis/blob/master/Silhouette%20Analysis.ipynb) talks about alternative ways of choosing the optimal number of clusters for the K-Means algorithm. More specifically, it shows how to perform silhouette analysis and plot the decision boundaries of K-Means for 2-dimensional data.

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