Скачать или смотреть Intro to AI - W4/1 Tanjung Lesung President University

Detail : The source provides an extensive overview of unsupervised learning, specifically focusing on the machine learning task of clustering. It begins by establishing that unsupervised learning identifies hidden structure in unlabeled data, contrasting this approach with classification, which relies on known labels for prediction. The text explores various academic perspectives on clustering, detailing several distinct modeling approaches, including prototype-based (k-means), density-based (DBSCAN), and hierarchical methods. Furthermore, it outlines practical applications for clustering across different sectors like retail, marketing, and HR, emphasizing its use in creating actionable segments for targeted decision-making. The explanation concludes by summarizing key algorithms and concepts, noting that the quality of clustering is judged by internal indices and its ultimate business usefulness.

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