{'fraction_non_alphanumeric': 0.012357414448669201, 'fraction_numerical': 0.0, 'mean_word_length': 5.927631578947368, '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}}
UTILIZING DATA MINING TO DISCOVER KNOWLEDGE IN CONSTRUCTION ENTERPRISE PERFORMANCE RECORDS

Abstract Although Data Mining (DM) has been applied extensively in information systems and identified as a crucial tool for automatic data analysis and enterprise knowledge inference, still the practicability of DM has been little explored in the construction industry in particular. This study is conducted using data from actual practice in the customer service department of the target enterprise. Starting with data preparation, decision tree analysis and domain knowledge inference, practical verification is performed and previously unknown knowledge is discovered. Two practical barriers to enterprise information mining are found: the separation of information among various information systems and lack of key information attributes. Hence, there is a significant limitation on practical information data mining to generate new information, and only with a proper information integration one can benefit from the potential practical application of DM.