## Secrets of successful data analysis - Sykalo Eugene 2023

# Unsupervised Learning - Techniques for training models with unlabeled data

Advanced Topics in Data Analysis

Unsupervised learning is a type of machine learning that involves training algorithms to identify patterns in unstructured data. This approach is different from supervised learning, which involves training algorithms with labeled data in order to make predictions about new data.

Unsupervised learning is important in data analysis because it can be used to identify hidden structures or relationships in data that might not be apparent through other means. This can lead to new insights and discoveries, and help organizations make better decisions.

There are many different types of unsupervised learning, including clustering and dimensionality reduction. Clustering algorithms are used to group similar data points together, while dimensionality reduction techniques are used to reduce the number of variables in a dataset.

In the following sections, we will explore the different types of unsupervised learning in more detail, as well as popular algorithms and evaluation techniques. We'll also examine some real-world applications of unsupervised learning in industry.

## Types of Unsupervised Learning

Unsupervised learning can be broadly classified into two types: clustering and dimensionality reduction.

### Clustering

Clustering is a technique used to group similar data points together based on their features. The aim of clustering is to find natural groupings in the data that may not be apparent through other means. Clustering is used in a variety of applications, including image segmentation, social network analysis, and market research.

### Types of Clustering Algorithms

There are several types of clustering algorithms, including:

- K-Means Clustering: This is one of the most popular clustering algorithms. It groups data points into a specified number of clusters based on the distance between the data points and the center of the cluster. The algorithm is iterative, and the number of clusters is typically specified by the user.
- Hierarchical Clustering: This algorithm creates a hierarchical tree of clusters. The tree can be represented as a dendrogram, which shows the relationships between the different clusters. In hierarchical clustering, the number of clusters is not specified in advance.
- Density-Based Clustering: This algorithm groups data points based on their density. It is particularly useful for identifying clusters of irregular shapes.

### Dimensionality Reduction

Dimensionality reduction is a technique used to reduce the number of variables in a dataset while retaining as much information as possible. This is important because in many datasets, there are often more variables than observations, which can lead to overfitting.

### Techniques for Dimensionality Reduction

There are several techniques for dimensionality reduction, including:

- Principal Component Analysis (PCA): This technique involves transforming the original variables into a new set of uncorrelated variables called principal components. These components are ordered by the amount of variance they explain in the data.
- Singular Value Decomposition (SVD): This technique is similar to PCA, but it can be used on non-square matrices. It is often used in natural language processing and image analysis.
- t-SNE: This technique is used for visualizing high-dimensional data in two or three dimensions. It works by preserving the pairwise distances between the data points in the low-dimensional space.

## Popular Unsupervised Learning Algorithms

### K-Means Clustering

K-Means Clustering is one of the most popular clustering algorithms. The algorithm works by partitioning data points into a specified number of clusters based on the distance between the data points and the center of the cluster. The algorithm is iterative and works by minimizing the sum of squared distances between the data points and their assigned cluster centers. The number of clusters is typically specified by the user.

One of the advantages of K-Means Clustering is that it is computationally efficient and can handle large datasets. However, it is sensitive to the initial placement of the cluster centers, which can result in suboptimal cluster assignments. It also assumes that the clusters are spherical and equally sized, which may not always be the case.

### Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a technique used for dimensionality reduction. The technique involves transforming the original variables into a new set of uncorrelated variables called principal components. These components are ordered by the amount of variance they explain in the data. PCA is useful for reducing the number of variables in a dataset while retaining as much information as possible.

One of the advantages of PCA is that it can be used to visualize high-dimensional data in two or three dimensions. It is also computationally efficient and can be applied to large datasets. However, PCA assumes that the data is linearly separable, which may not always be the case. It also assumes that the principal components are orthogonal, which may not always be true.

### Hierarchical Clustering

Hierarchical Clustering is an algorithm that creates a hierarchical tree of clusters. The tree can be represented as a dendrogram, which shows the relationships between the different clusters. In hierarchical clustering, the number of clusters is not specified in advance. The algorithm works by iteratively merging the most similar clusters until all the data points belong to a single cluster.

One of the advantages of Hierarchical Clustering is that it does not require the number of clusters to be specified in advance. It is also useful for identifying clusters of different sizes and shapes. However, it can be computationally expensive and may not be suitable for large datasets.

### Singular Value Decomposition (SVD)

Singular Value Decomposition (SVD) is a technique similar to PCA, but it can be used on non-square matrices. It is often used in natural language processing and image analysis. The technique involves decomposing a matrix into three components: the left singular vectors, the singular values, and the right singular vectors. The singular values represent the amount of variance explained by each component.

One of the advantages of SVD is that it can be used on non-square matrices, which makes it useful for a variety of applications. It is also computationally efficient and can be applied to large datasets. However, SVD assumes that the data is linearly separable, which may not always be the case. It also requires the data to be centered and scaled, which can be a preprocessing challenge.

## Techniques for Evaluating Unsupervised Learning Algorithms

Evaluating unsupervised learning algorithms can be challenging because there is no ground truth against which to compare the results. However, there are several techniques that can be used to evaluate the performance of unsupervised learning algorithms.

### Internal Evaluation Metrics

Internal evaluation metrics are used to evaluate the performance of a clustering algorithm based on the properties of the data itself. These metrics do not require any external information about the data.

### Silhouette Coefficient

The Silhouette Coefficient is a metric that measures how similar an object is to its own cluster compared to other clusters. The coefficient ranges from -1 to 1, with values close to 1 indicating well-clustered data and values close to -1 indicating poorly-clustered data. The Silhouette Coefficient is often used to determine the optimal number of clusters in a dataset.

### Calinski-Harabasz Index

The Calinski-Harabasz Index is another internal evaluation metric that measures the ratio of between-cluster variance to within-cluster variance. A higher value indicates better clustering results.

### External Evaluation Metrics

External evaluation metrics are used to evaluate the performance of a clustering algorithm based on external information about the data. This information can come from expert labels or other sources of ground truth.

### Adjusted Rand Index

The Adjusted Rand Index is a metric that measures the similarity between the predicted clustering assignments and the true clustering assignments. The index ranges from -1 to 1, with values close to 1 indicating good clustering results.

### Normalized Mutual Information

The Normalized Mutual Information is another external evaluation metric that measures the mutual information between the predicted clustering assignments and the true clustering assignments, normalized by the entropy of the assignments. A higher value indicates better clustering results.

## Applications of Unsupervised Learning in Industry

Unsupervised learning has a wide range of applications in industry, from anomaly detection to customer segmentation. In this section, we will explore some of the most common applications of unsupervised learning in industry.

### Anomaly Detection

Anomaly detection is the process of identifying data points that deviate from the norm. This is important in many industries, including finance, healthcare, and cybersecurity. Unsupervised learning algorithms can be used for anomaly detection by identifying patterns in the data that are different from the majority of the data.

One technique for anomaly detection is clustering. Clustering can be used to identify data points that are dissimilar to other data points in the same cluster. Another technique is density-based anomaly detection, which involves identifying data points that are in low-density regions of the data.

### Customer Segmentation

Customer segmentation is the process of dividing customers into groups based on their characteristics and behaviors. This is important in industries such as marketing and retail, where companies want to tailor their products and services to specific groups of customers.

Unsupervised learning algorithms can be used for customer segmentation by identifying similarities and differences between customers. Clustering algorithms can be used to group customers based on their purchasing history, demographics, and other characteristics. Dimensionality reduction techniques can be used to reduce the number of variables in the data, making it easier to identify patterns.

### Image and Text Analysis

Unsupervised learning algorithms can also be used for image and text analysis. In image analysis, unsupervised learning algorithms can be used for tasks such as image segmentation and object recognition. In text analysis, unsupervised learning algorithms can be used for tasks such as topic modeling and sentiment analysis.

One technique for image analysis is convolutional autoencoders, which can be used for image denoising and reconstruction. Another technique is k-means clustering, which can be used for image segmentation.

In text analysis, one technique is latent Dirichlet allocation (LDA), which is a topic modeling technique that can be used to identify topics in a corpus of text. Another technique is word embeddings, which can be used to represent words as vectors in a high-dimensional space.

### Fraud Detection

Fraud detection is the process of identifying fraudulent activity in financial transactions. Unsupervised learning algorithms can be used for fraud detection by identifying patterns in the data that are different from normal behavior.

One technique for fraud detection is clustering, which can be used to identify groups of transactions that are dissimilar to other transactions. Another technique is density-based anomaly detection, which can be used to identify transactions that occur in low-density regions of the data.

### Recommendation Systems

Recommendation systems are used to suggest products or services to customers based on their preferences and past behavior. Unsupervised learning algorithms can be used for recommendation systems by identifying similarities between customers and products.

One technique for recommendation systems is collaborative filtering, which involves analyzing the preferences of multiple customers to make recommendations. Another technique is matrix factorization, which can be used to identify latent factors that influence customer preferences.