{'fraction_non_alphanumeric': 0.025437201907790145, 'fraction_numerical': 0.0, 'mean_word_length': 5.3, '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}}
Tree-based clustering for gene expression data

Data clustering methods have been proven to be a successful data mining technique in analysis of gene expression data and many other types of data. However, some concerns and challenges still remain, e.g., in gene expression clustering. In this paper, we propose an efficient clustering method using attractor trees. The combination of the density-based approach and the similarity-based approach considers clusters with diverse shapes, densities, and sizes. Experiments on gene expression datasets demonstrate that our approach is efficient and scalable with competitive accuracy.