# Introduction to Clustering

A Summary of lecture "Cluster Analysis in Python", via datacamp

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
```

## Unsupervised learning: basics

- What is unsupervised learning?
- A group of machine learning algorithm that find patterns in data
- Data for algorithms has not been labeled, classified or characterized
- The objective of the algorithm is to interpret any structure in the data
- Common unsupervised learning algorithms : Clustering, neural network, anomaly detection

- What is clustering?
- The process of grouping items with similar characteristics
- Items in groups similar to each other than in other groups
- Example: distance between points on a 2D plane

```
x = [9, 6, 2, 3, 1, 7, 1, 6, 1, 7, 23, 26, 25, 23, 21, 23, 23, 20, 30, 23]
y = [8, 4, 10, 6, 0, 4, 10, 10, 6, 1, 29, 25, 30, 29, 29, 30, 25, 27, 26, 30]
```

```
plt.scatter(x, y)
```

## Basics of cluster analysis

- What is a cluster?
- A group of items with similar characteristics
- Google News: articles where similar words and word associations appear together
- Customer Segments

- Clustering Algorithms
- Hierarchical Clustering
- K-means Clustering
- Other clustering algorithms: DBSCAN, Gaussian Methods

### Pokémon sightings: hierarchical clustering

We are going to continue the investigation into the sightings of legendary Pokémon from the previous exercise. Remember that in the scatter plot of the previous exercise, you identified two areas where Pokémon sightings were dense. This means that the points seem to separate into two clusters. In this exercise, you will form two clusters of the sightings using hierarchical clustering.

```
df = pd.DataFrame({'x': x, 'y': y})
```

```
from scipy.cluster.hierarchy import linkage, fcluster
# Use the linkage() to compute distance
Z = linkage(df, 'ward')
# Generate cluster labels
df['cluster_labels'] = fcluster(Z, 2, criterion='maxclust')
# Plot the points with seaborn
sns.scatterplot(x='x', y='y', hue='cluster_labels', data=df)
```

### Pokémon sightings: k-means clustering

We are going to continue the investigation into the sightings of legendary Pokémon from the previous exercise. Just like the previous exercise, we will use the same example of Pokémon sightings. In this exercise, you will form clusters of the sightings using k-means clustering.

```
df = df.astype('float')
```

```
from scipy.cluster.vq import kmeans, vq
# Compute cluster centers
centroids, _ = kmeans(df, 2)
# Assign cluster labels
df['cluster_labels'], _ = vq(df, centroids)
# Plot the points with seaborn
sns.scatterplot(x='x', y='y', hue='cluster_labels', data=df)
```

## Data preparation for cluster analysis

- Why do we need to prepare data for clustering?
- Variables have incomparable units
- Variables with same units have vastly different scales and variances
- Data in raw form may lead to bias in clustering
- Clusters may be heavily dependent on one variable
- Solution: normalization of individual variables

```
from scipy.cluster.vq import whiten
goals_for = [4, 3, 2, 3, 1, 1, 2, 0, 1, 4]
# Use the whiten() function to standardize the data
scaled_data = whiten(goals_for)
print(scaled_data)
```

```
plt.plot(goals_for, label='original')
plt.plot(scaled_data, label='scaled')
plt.legend()
plt.savefig('../images/scaled_data.png')
```

```
rate_cuts = [0.0025, 0.001, -0.0005, -0.001, -0.0005, 0.0025, -0.001, -0.0015, -0.001, 0.0005]
# use the whiten() to standardize the data
scaled_data = whiten(rate_cuts)
plt.plot(rate_cuts, label='original')
plt.plot(scaled_data, label='scaled')
plt.legend()
```

### FIFA 18: Normalize data

FIFA 18 is a football video game that was released in 2017 for PC and consoles. The dataset that you are about to work on contains data on the 1000 top individual players in the game. You will explore various features of the data as we move ahead in the course. In this exercise, you will work with two columns, `eur_wage`

, the wage of a player in Euros and `eur_value`

, their current transfer market value.

- Preprocess

```
fifa = pd.read_csv('./dataset/fifa_18_sample_data.csv')
fifa.columns
```

```
fifa['scaled_wage'] = whiten(fifa['eur_wage'])
fifa['scaled_value'] = whiten(fifa['eur_value'])
fifa.plot(x='scaled_wage', y='scaled_value', kind='scatter');
# Check mean and standard deviation of scaled values
print(fifa[['scaled_wage', 'scaled_value']].describe())
```