{'fraction_non_alphanumeric': 0.016560509554140127, 'fraction_numerical': 0.003821656050955414, 'mean_word_length': 5.442622950819672, '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}}
Synthesis of Datasets with Specific Characteristics for the Clustering Problem

In this paper, we propose a method for synthesis of datasets with specific characteristics for the clustering task. Namely, we propose an algorithm, which can generate a clustering dataset given its meta-feature description. The method we propose is based on an evolutionary algorithm with crossover and mutation operators that are capable to improve candidate datasets in a natural way. We experimentally compared this method with two other approaches for dataset synthesis. We used meta-feature vectors of 247 real-world datasets as inputs. The proposed method outperformed existing ones with respect to Mahalanobis distance between target meta-feature vectors and characteristics of generated datasets.