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Demonstrate with diagram the process of KDD.

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The term KDD stands for Knowledge Discovery in Databases. It refers to the broad procedure of discovering knowledge in data and emphasizes the high-level applications of specific Data Mining techniques. It is a field of interest to researchers in various fields, including artificial intelligence, machine learning, pattern recognition, databases, statistics, knowledge acquisition for expert systems, and data visualization.

  1. Understanding the Application Domain:

    • Define goals and objectives of KDD.
    • Understand end-user needs and the environment.
  2. Choosing and Creating a Data Set:

    • Determine relevant data for KDD.
    • Integrate data from various sources for analysis.
  3. Preprocessing and Cleansing:

    • Improve data reliability.
    • Handle missing values, noise, and outliers.
  4. Data Transformation:

    • Prepare data for Data Mining.
    • Include dimension reduction and attribute transformation.
  5. Prediction and Description:

    • Decide on Data Mining techniques based on objectives.
    • Choose between prediction (supervised) and description (unsupervised).
  6. Selecting the Data Mining Algorithm:

    • Choose a specific algorithm based on the selected technique.
    • Consider factors like precision, understandability, and parameter settings.
  7. Utilizing the Data Mining Algorithm:

    • Implement the chosen algorithm.
    • Iterate as needed by adjusting control parameters.
  8. Evaluation:

    • Assess and interpret mined patterns and rules.
    • Consider the impact of preprocessing steps on results.
  9. Using the Discovered Knowledge:

    • Incorporate knowledge into systems for action.
    • Measure the effectiveness of changes made based on the discovered knowledge.
    • Address challenges in applying knowledge to dynamic real-world conditions.

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