What is ML Clustering? – Lesson Preview
Clustering is one of the most versatile and insightful techniques in machine learning and this lesson explains exactly how it works and why it matters for marketers and SEOs. You’ll learn how clustering helps you uncover structure in unlabelled data, revealing hidden groupings across customers, keywords or web pages without needing predefined categories.
Through clear explanations and marketing-focused examples, you’ll discover the main types of clustering algorithms, from beginner-friendly centroid-based methods like K-Means to more advanced density, distribution, hierarchical, and grid-based models. You’ll also learn when to use each, how they handle different data types (numeric, text, or even images), and what makes each approach valuable for real-world SEO applications.
By the end of this lesson, you’ll understand the building blocks of clustering, know which algorithm suits your data, and be ready to explore hands-on exercises and readings that connect theory to practice.
What you’ll learn (why it matters)
- Understand ML clustering — because segmenting unlabelled data reveals actionable patterns.
- Differentiate hard vs soft clustering — because choosing the right method affects your segmentation accuracy.
- Identify main clustering types — because each approach fits a different kind of marketing or SEO data.
- Recognize key algorithms like K-Means, DBSCAN, GMM — because they’re foundational for text, numeric, and image clustering.
- Prepare text data for clustering — because SEO work often relies on unstructured text inputs.
Key concepts (with mini-definitions)
- Clustering — unsupervised ML method grouping similar data points into clusters.
- Unsupervised learning — learning patterns from unlabelled data without known outcomes.
- Hard clustering — each data point belongs to only one cluster.
- Soft (fuzzy) clustering — data points can belong to multiple clusters with probabilities.
- Centroid-based clustering — groups data by proximity to central points (centroids).
- Density-based clustering — groups dense areas of data, ignoring outliers.
- Distribution-based clustering — models data as overlapping probabilistic distributions (e.g., Gaussian).
- Hierarchical clustering — builds clusters in a tree-like structure.
Tools mentioned
K-Means, DBSCAN, OPTICS, Mean Shift, Gaussian Mixture Model (GMM), LDA, BIRCH and CLIQUE.
Practice & readings
- Review the clustering algorithms summary resource linked after the lesson.
- Explore Python tutorials to test algorithms on your own data.
- Recommended reading: guides on topic modeling and text clustering for SEO.
Key insights & takeaways
- Clustering turns raw, unlabelled data into structured, actionable insights.
- Different algorithms excel with different data shapes, densities, and types.
- Soft clustering captures nuance in customer or keyword behavior.
- Text clustering requires transforming language into numeric embeddings.
- Most marketing data benefits from exploratory clustering before predictive modelling.
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