{"slug": "unsupervised-learning-in-ml", "title": "Unsupervised learning in ML", "summary": "A developer explains unsupervised learning, a machine learning approach in which algorithms identify patterns, groups, and relationships in unlabeled data without predefined answers. The writeup covers clustering techniques including hierarchical clustering (agglomerative and divisive), dendrograms, and DBSCAN, as well as dimensionality reduction via Principal Component Analysis (PCA).", "body_md": "**Unsupervised learning** is a type of machine learning where a computer learns from data that does not have predefined labels or answers. Instead of being told what the correct result is, the algorithm studies the data and identifies patterns, groups, or relationships on its own.\n\nUnsupervised learning is useful when there is a large amount of data but no labelled information available. It helps organizations discover hidden patterns and gain a better understanding of their data.\n\nThe two major areas of unsupervised learning discussed in this article are:\n\n**Clustering** is an unsupervised learning technique that groups similar data points together. Data points within the same group, known as a **cluster**, are more similar to each other than to data points in other groups.\n\nClustering can be used for:\n\n**Hierarchical clustering** is a clustering technique that creates a hierarchy of groups. Instead of simply dividing data into a fixed number of clusters, it shows how individual data points and groups are related at different levels.\n\nThere are two main approaches to hierarchical clustering:\n\n**Agglomerative clustering** is a **bottom-up approach**.\n\nIt starts with each data point as its own individual cluster. The algorithm then repeatedly combines the most similar clusters until all the data points form one large cluster or the desired grouping is reached.\n\n**Process:**\n\n**Individual Data Points → Small Clusters → Larger Clusters → One Large Cluster**\n\nAgglomerative clustering is the more commonly used approach to hierarchical clustering.\n\n**Divisive clustering** is a **top-down approach**.\n\nIt begins with all data points in one large cluster. The algorithm then repeatedly divides the cluster into smaller groups until individual groups are formed or the desired number of clusters is reached.\n\n**One Large Cluster → Smaller Clusters → Individual Groups**\n\n| Approach | Direction | Starting Point | \n|---|---|---|\n| **Agglomerative** | Bottom-up | Individual data points | \n| **Divisive** | Top-down | One large cluster | \n\nA **dendrogram** is a tree-like diagram used to display the results of hierarchical clustering.\n\nIt shows how individual data points or groups are combined or divided at different levels.\n\nThe height at which two groups are connected represents how different or distant they are. By choosing a particular level on the dendrogram, we can determine how many clusters to create.\n\n```\n              ┌───────────────┐\n              │               │\n          ┌───┴───┐       ┌───┴───┐\n          │       │       │       │\n          A       B       C       D\n```\n\nHere, A and B are grouped together, while C and D form another group.\n\nDendrograms help users:\n\n**DBSCAN** stands for **Density-Based Spatial Clustering of Applications with Noise**.\n\nIt is a clustering algorithm that groups data points based on how closely packed they are. It can identify areas with many data points while treating points that do not belong to a dense group as **noise or outliers**.\n\nUnlike some clustering methods, DBSCAN does not require the user to specify the exact number of clusters beforehand.\n\nDBSCAN looks for areas where data points are close together.\n\nIt generally identifies three types of points:\n\n**Dimensionality reduction** is the process of reducing the number of variables or features in a dataset while trying to preserve the most important information.\n\nDimensionality reduction transforms the data into fewer dimensions while attempting to retain its important patterns.\n\n**Principal Component Analysis (PCA)** is one of the most widely used dimensionality reduction techniques.\n\nPCA transforms a dataset with many variables into a smaller number of new variables called **principal components**.\n\nThe principal components are created so that they capture as much of the important variation in the original data as possible.\n\nPCA can be used for:\n\nBusinesses can use clustering to divide customers into groups based on their purchasing habits, interests, or behaviour. This helps businesses understand different customer groups.\n\nUnsupervised learning can help identify unusual transaction patterns and discover groups of customers with similar financial behaviour.\n\nHealthcare organizations can analyze patient data to identify groups with similar characteristics or discover patterns in medical information.\n\nRetail businesses can analyze purchasing behaviour to identify groups of customers and discover which products are commonly purchased together.\n\nClustering can be used to group similar pixels or identify patterns within images. Dimensionality reduction can also help simplify complex image data.\n\nUnsupervised learning can identify unusual patterns in network activity that may require further investigation.\n\nOrganizations can analyze large amounts of social media information to identify groups, topics, trends, or patterns in user behavior.\n\nManufacturers can use unsupervised learning to identify unusual machine behaviour and discover patterns that may indicate equipment problems.", "url": "https://wpnews.pro/news/unsupervised-learning-in-ml", "canonical_source": "https://dev.to/anyangiii/unsupervised-learning-in-ml-4il6", "published_at": "2026-09-21 06:04:39+00:00", "updated_at": "2026-09-21 06:23:03.910894+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["DBSCAN", "Principal Component Analysis"], "alternates": {"html": "https://wpnews.pro/news/unsupervised-learning-in-ml", "markdown": "https://wpnews.pro/news/unsupervised-learning-in-ml.md", "text": "https://wpnews.pro/news/unsupervised-learning-in-ml.txt", "jsonld": "https://wpnews.pro/news/unsupervised-learning-in-ml.jsonld"}}