{'fraction_non_alphanumeric': 0.021922428330522766, 'fraction_numerical': 0.0, 'mean_word_length': 4.823529411764706, '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}}
Representative points for location-biased datasets

ABSTRACT Representative points (RPs) are a set of points that optimally represents a distribution in terms of mean square error. When the prior data is location biased, the direct methods such as the k-means algorithm may be inefficient to obtain the RPs. In this article, a new indirect algorithm is proposed to search the RPs based on location-biased datasets. Such an algorithm does not constrain the parameter model of the true distribution. The empirical study shows that such algorithm can obtain better RPs than the k-means algorithm.