Detecting Patterns with Unsupervised Learning

In this chapter, we are going to learn about unsupervised learning and how to use it in real-world situations. By the end of this chapter, you will have a better understanding of the following topics:

• Unsupervised learning definition
• Clustering data with the K-Means algorithm
• Estimating the number of clusters with the Mean Shift algorithm
• Estimating the quality of clustering with silhouette scores
• Gaussian Mixture Models
• Building a classifier based on Gaussian Mixture Models
• Finding subgroups in stock markets the using Affinity Propagation model
• Segmenting the market based on shopping patterns

What is unsupervised learning?

Unsupervised learning refers to the process of building machine learning models without using labeled training data. Unsupervised learning finds applications in diverse fields of study, including market segmentation, stock markets, natural language processing, and computer vision, to name a few.

In the previous chapters, we were dealing with data that had labels associated with it. When we have labeled training data, algorithms learn to classify data based on those labels. In the real world, labeled data might not always be available.

Sometimes, a large quantity of data exists without labeling and it needs to be categorized in some way. This is the perfect use case for unsupervised learning. Unsupervised learning algorithms attempt to classify data into subgroups within a given dataset using some similarity metric.

When we have a dataset without any labels, we assume that the data is generated because of latent variables that govern the...

Clustering data with the K-Means algorithm

Clustering is one of the most popular unsupervised learning techniques. This technique is used to analyze data and find clusters within that data. In order to find these clusters, we use a similarity measurement such as the Euclidean distance to find subgroups. This similarity measure can estimate the tightness of a cluster. Clustering is the process of organizing data into subgroups whose elements are like each other.

The goal of the algorithm is to identify the intrinsic properties of data points that make them belong to the same subgroup. There is no universal similarity metric that works in all cases. For example, we might be interested in finding the representative data point for each subgroup, or we might be interested in finding the outliers in the data. Depending on the situation, different metrics might be more appropriate than others.

The K-Means algorithm is a well-known algorithm for clustering data...

What are Gaussian Mixture Models?

Before we discuss Gaussian Mixture Models (GMMs), let's first understand what a Mixture Model is. A Mixture Model is a type of probability density model where it is assumed that the data is governed by several component distributions. If these distributions are Gaussian, then the model becomes a Gaussian Mixture Model. These component distributions are combined in order to provide a multi-modal density function, which becomes a mixture model.

Let's look at an example to understand how Mixture Models work. We want to model the shopping habits of all the people in South America. One way to do it would be to model the whole continent and fit everything into a single model, but people in different countries shop differently. We therefore need to understand how people in individual countries shop and how they behave.

To get a good representative model, we need to account for all the variations within the continent. In this case, we can use...

Finding subgroups in stock market using the Affinity Propagation model

Affinity Propagation is a clustering algorithm that doesn't require a number of clusters to be specified beforehand. Because of its generic nature and simplicity of implementation, it has found a lot of applications in many fields. It finds out representative clusters, called exemplars, using a technique called message passing. It starts by specifying the measures of similarity that need to be considered. It simultaneously considers all training data points as potential exemplars. It then passes messages between the data points until it finds a set of exemplars.

The message passing happens in two alternate steps, called responsibility and availability. Responsibility refers to the message sent from members of the cluster to candidate exemplars, indicating how well suited the data point would be as a member of this exemplar's cluster. Availability refers to the message sent from candidate exemplars...

Segmenting the market based on shopping patterns

Let's see how to apply unsupervised learning techniques to segment the market based on customer shopping habits. You have been provided with a file named sales.csv. This file contains the sales details of a variety of tops from several retail clothing stores. The goal is to identify the patterns and segment the market based on the number of units sold in those stores.

Create a new Python file and import the following packages:

import csv

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import MeanShift, estimate_bandwidth

Load the data from the input file. Since it's a CSV file, we can use the csv reader in Python to read the data from this file and convert it into a NumPy array:

# Load data from input file
input_file = 'sales.csv'
file_reader = csv.reader(open(input_file, 'r'), delimiter=',')
X = []
for count, row in enumerate...

Summary

In this chapter, we started by discussing unsupervised learning and its applications. We then learned about clustering and how to cluster data using the K-Means algorithm. We discussed how to estimate the number of clusters with the Mean Shift algorithm. We talked about silhouette scores and how to estimate the quality of clustering. We learned about Gaussian Mixture Models and how to build a classifier based on them. We also discussed the Affinity Propagation model and used it to find subgroups within the stock market. We then applied the Mean Shift algorithm to segment the market based on shopping patterns.

In the next chapter, we will learn how to build a recommendation engine.