{'fraction_non_alphanumeric': 0.01591187270501836, 'fraction_numerical': 0.0, 'mean_word_length': 5.760330578512397, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Learning a Metric during Hierarchical Clustering based on Constraints

Constrained clustering has many useful applications. In this paper, we consider applications, in which a hierarchical target structure is preferable. Therefore, we constrain a hierarchical agglomerative clustering through the use of MLB constraints, which provide information about hierarchical relations between objects. We propose an algorithm that learns a suitable metric according to the constraint set by modifying the metric whenever the clustering process violates a constraint. We furthermore combine this approach with an instance-based constrained clustering to further improve the cluster quality. Both approaches have proven to be very successful in a semi-supervised setting, in which constraints do not cover all existing clusters.