COMPREHENSIVE REVIEW OF KNOWLEDGE DISCOVERY TECHNIQUES: A COMPARATIVE ANALYSIS
Keywords:
Data Mining, Knowledge Discovery, TechniquesAbstract
Knowledge discovery is a new topic in which we integrate approaches from algorithmics, artificial intelligence, mathematics, and statistics to address theoretical and practical difficulties related to extracting knowledge, i.e., new ideas or concept links concealed in large amounts of raw data. Knowledge discovery enables the automation of difficult search and data analysis operations.
The key stage of the knowledge discovery process is data mining. It entails finding information nuggets, such as relevant patterns, pattern correlations, estimates, or rules, buried in large amounts of data. The retrieved knowledge nuggets will be utilized to validate hypotheses or to forecast and explain knowledge.
The methodology includes numerous data exploration situations, and DM methods are applied in their scope. The study lays the groundwork for the use of DM approaches in industry. The development of approaches for knowledge discovery from data is also described, as is a categorization of the most common Data Mining methods, split by kind of completed tasks. The presentation of chosen Data Mining approaches in mechanical engineering summarizes the study.
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