{'fraction_non_alphanumeric': 0.016375545851528384, 'fraction_numerical': 0.0, 'mean_word_length': 5.792592592592593, '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 techniques and their applications in engineering

Abstract Clustering is an important data exploration technique with many applications in different areas of engineering, including engineering design, manufacturing system design, quality assurance, production planning and process planning, modelling, monitoring, and control. The clustering problem has been addressed by researchers from many disciplines. However, efforts to perform effective and efficient clustering on large data sets only started in recent years with the emergence of data mining. The current paper presents an overview of clustering algorithms from a data mining perspective. Attention is paid to techniques of scaling up these algorithms to handle large data sets. The paper also describes a number of engineering applications to illustrate the potential of clustering algorithms as a tool for handling complex real-world problems.