[post_excerpt]
Unlabeled data ocurrences of many types
Grouping of data points into pre-provided or spontaneous groups based on similarity
To correctly assign each ocurrence to an inherent or provided cluster
Classification algorithms adapted for multi-label output
Hamming loss, precision, recall, and F1 score for multi-label settings.
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