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Clustering aims to partition a dataset into subsets (clusters), such that data points within the same cluster exhibit high intra-cluster similarity, while points in different clusters exhibit low inter-cluster similarity.
Expected Outcome
In essence, the clustering model outputs a partition of dataset into groups that share similar characteristics
Benefits
Unsupervised Pattern Discovery: Clustering identifies hidden structures or natural groupings in unlabeled data without prior knowledge or supervision.
Data Simplification and Summarization: It reduces the complexity of large datasets by grouping similar data points, making it easier to analyze and interpret.
Anomaly Detection: Clustering aids in anomaly detection by identifying normal groupings within a dataset, making it easier to spot data points that deviate significantly from the norm. These outliers often indicate unusual or rare events such as fraud, system faults, or other anomalies that require attention. Read More SAP Developers
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