{'fraction_non_alphanumeric': 0.017759562841530054, 'fraction_numerical': 0.001366120218579235, 'mean_word_length': 4.817460317460317, '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}}
Research on Greedy Clique Partition-GCP Algorithm

Clustering of binary fingerprints is used in the classification of gene expression data. It is known that the clustering of binary fingerprints with 3 bits of missing value is NP-hard. The greedy clique partition (GCP for short) algorithm is a heuristic algorithm used to clustering of binary fingerprints with missing values. In this paper, we firstly study the feature of instances which can not be resolved by the GCP based on hash table. Then a new property of problem instances is given, which can further improve the heuristic algorithm based on linked list. Finally, an empirical formula is presented, which is used to judge the accuracy and credibility of the GCP algorithm