Clustering, anomaly detection and dimension reduction #ai #machinelearning #clustering #kmeans

21 May 2025 · 1 min

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In this concise tutorial, the speaker introduces unsupervised learning as a vital complement to supervised methods, emphasizing its reliance on unlabeled data to discover inherent patterns. The discussion outlines three core unsupervised techniques: clustering, exemplified by the K‑means algorithm that groups data based on similarity; anomaly/outlier detection, highlighted through the Isolation Forest method that isolates atypical observations; and dimensionality reduction, illustrated with Principal Component Analysis (PCA) which compresses high‑dimensional feature spaces (such as pixel‑rich images) while preserving essential information. The monologue serves as a high‑level overview, defining each technique, its primary purpose, and a representative algorithm, establishing a foundational understanding for learners new to unsupervised machine‑learning concepts.

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Although supervised learning is the most common method used to train machine learning models, unsupervised learning is just as important. Sometimes it's even necessary. Unsupervised learning uses unlabeled data. This means that the model has to manage on its own to find recurring patterns and structures in the data. The most popular application of unsupervised learning is clustering. The model attempts to segment the data into separate classes based on similarities it can identify. The most famous algorithm for clustering is called K-means. In addition to clustering, there are other applications in the field of unsupervised learning, namely anomaly and outlier detection. This involves identifying data whose characteristics are too different from those of the rest of the batch. The reference algorithm of this kind of application is called Isolation Forest. There's also dimension reduction, which consists in reducing the number of features while keeping a satisfying predictive quality. This is the case, for example, with images containing thousands of pixels, where each pixel is seen as a feature. The objective in this case is to reduce the number of pixels without losing too much essential information. One of the best-known algorithms is Principal Component Analysis, or PCA.