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Knowledge Discovery in Databases

University of New South Wales_022724A
[University of New South Wales, Australia]


- Overview

Knowledge discovery in databases (KDD) is the process of finding patterns in data that are useful, novel, and understandable. KDD is also known as data mining. 

KDD is a general process that involves:

  • Finding patterns in a dataset
  • Mapping low-level data into other forms
  • Finding, transforming, and refining meaningful patterns and data
  • Representing discovered knowledge in a meaningful and actionable form


The first step in the KDD process is data selection, which involves gathering data from various sources to form a raw dataset. The final step is knowledge representation, which involves creating visualizations using various graphical representation methods. 

KDD can have many advantages, including: 

  • Improving decision-making
  • Increasing efficiency
  • Better customer service
  • Fraud detection
  • Predictive modeling


However, KDD can also have some disadvantages, including: privacy concerns, complexity, unintended consequences, data quality, high cost, overfitting.

 

[More to come ...]

 

 

 

 

 

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