{'fraction_non_alphanumeric': 0.02857142857142857, 'fraction_numerical': 0.0, 'mean_word_length': 5.448, '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}}
Review of data mining for multi-disciplinary design optimization

One of the difficulties in multi-disciplinary design optimization lies in the complicated interactions between large numbers of objective functions, design variables, and constraints. This difficulty often leads to an unsuitable formulation of design problems. Data mining is often used to address these challenges. Data mining provides insight into the design of complicated systems. The information obtained from data mining can be used to support (a) formulation of design problems, (b) decision making, and (c) design steering. This report presents a review of recent developments and applications of data mining techniques in the engineering design field, and introduces real-world examples of state-of-the-art data mining techniques.