Machine Learning (ML) in Bioinformatics

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Unsupervised learning algorithms

Prerequisites: None.

Welcome to the unsupervised learning module! In this module, we will cover the basics of unsupervised learning algorithms and how they differ from supervised learning algorithms. We will also explore some standard unsupervised learning techniques and their applications.

What is Unsupervised Learning?

Unsupervised learning is a machine learning type where we don't give the model any labeled training data. Instead, the model is only given a dataset and must find patterns and relationships within the data. Unsupervised learning algorithms are useful for clustering, anomaly detection, and dimensionality reduction task

Unsupervised learning algorithms differ from supervised learning algorithms because they do not have a target or output variable they are trying to predict. In contrast, supervised learning algorithms use labeled training data to learn a function that maps input data to the desired output.

Some standard unsupervised learning techniques include:

Clustering:

Clustering algorithms group data points together based on their similarity. Standard clustering algorithms include k-means, hierarchical clustering, and density-based clustering.

Anomaly detection:

Anomaly detection algorithms identify data points that are unusual or do not fit the pattern of the rest of the data. These algorithms are often used for fraud detection or identifying errors in manufacturing processes.

Dimensionality reduction:

Dimensionality reduction algorithms reduce the number of features or dimensions in a dataset while retaining as much information as possible. Reducing dimensions can make it easier to visualize and analyze the data. Standard dimensionality reduction techniques include principal component analysis (PCA) and singular value decomposition (SVD).

Applications of Unsupervised Learning

Unsupervised learning algorithms have a wide range of applications, including:

Customer segmentation:

Clustering algorithms are used to group customers based on their purchasing behavior or other characteristics, allowing businesses to tailor their marketing efforts to specific groups.

Fraud detection:

Anomaly detection algorithms identify unusual patterns in financial transactions or other data that may indicate fraud.

Image recognition:

Dimensionality reduction algorithms reduce the number of features in an image, making it easier to analyze and classify the image using supervised learning algorithms.

Natural language processing:

Unsupervised learning algorithms can identify patterns and relationships in large volumes of unstructured text data, such as customer reviews or social media posts.