10 papers analyzed
These studies suggest data mining is an interdisciplinary process that discovers valuable patterns and structures in large datasets, aiding business decisions, detecting fraud, and improving various industry functions.
Data mining is the interdisciplinary process of discovering patterns, anomalies, and valuable information from large datasets. It integrates techniques from statistics, machine learning, database technology, and other fields to extract actionable insights. This synthesis explores the key insights from various research papers on data mining, focusing on its techniques, applications, and challenges.
Definition and Scope of Data Mining:
Techniques and Methods:
Applications in Business and Industry:
Challenges and Issues:
Interdisciplinary Nature:
Data mining is a powerful and interdisciplinary field that extracts valuable insights from large datasets using a variety of techniques. It has significant applications in business and industry, enabling better decision-making and predictive analysis. However, it also faces challenges related to data quality and the complexity of modern datasets. The integration of methods from multiple disciplines continues to enhance its effectiveness and scope.
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