{'fraction_non_alphanumeric': 0.017673048600883652, 'fraction_numerical': 0.0, 'mean_word_length': 5.181818181818182, '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}}
Estimating incremental dimensional algorithm with sequence data set

Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. In this paper, the scholar proposes a new approach for robust hierarchical clustering based on the distance function between each data object and the cluster centers. This method avoids the need to compute the distance of each data object to the cluster center. It saves running time. The experimental results showed that the best clusters were obtained using EIDA method, this suggests that this similarity measure would be applicable to sequence data sets.