{'fraction_non_alphanumeric': 0.020440251572327043, 'fraction_numerical': 0.0, 'mean_word_length': 5.434343434343434, '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}}
Clustering Big Cancer Data by Effect Sizes

We propose an effect size based approach to compute initial dissimilarities for Ensemble Algorithm of Clustering Cancer Data (EACCD). The proposed method is applied to the colon cancer data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute and compared with the log-rank approach where initial dissimilarities are computed from the log-rank test statistic. The experimental results show that under the proportional hazards assumption, the effect size approach generates robust results and has a better performance than the log-rank approach.