{'fraction_non_alphanumeric': 0.019450800915331808, 'fraction_numerical': 0.004576659038901602, 'mean_word_length': 5.8359375, '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}}
Correlation Weighted Heterogeneous Euclidean-Overlap Metric

Abstract Many data mining algorithms depend on a good distance function to be successful. Among large numbers of distance functions, Heterogeneous Euclidean-Overlap Metric (simply HEOM) is the simplest but effective distance function to handle the applications with both continuous and nominal attributes. In order to scale up its generalization performance, we present an improved HEOM by correlation weighting. We call our improved HEOM correlation weighted Heterogeneous Euclidean-Overlap Metric (simply CWHEOM) in this paper. In CWHEOM, to discrete and continuous class problems, we apply different correlation functions to estimate the correlation between attribute variables and class variable. Experiments running on 36 discrete class data sets and 36 continuous class data sets validate its effectiveness.