{'fraction_non_alphanumeric': 0.02091503267973856, 'fraction_numerical': 0.0, 'mean_word_length': 5.900900900900901, '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}}
Cluster Description Formats, Problems and Algorithms

Clustering is one of the major data mining tasks. So far, the database and data mining literature lacks systematic study of cluster descriptions, which are essential to provide the user with understandable knowledge of the clusters and support further interactive exploration. In this paper, we introduce novel description formats leading to more descriptive power. We define two alternative problems of generating cluster descriptions, Minimum Description Length and Maximum Description Accuracy, providing different trade-offs between interpretability and accuracy. We also present heuristic algorithms for both problems, together with their empirical evaluation and comparison to state-of-the-art algorithms.