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Explain Data Mining as a step in KDD. Give the architecture of typical Data Mining system?
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KDD Process (Knowledge Discovery in Database):

  • The term KDD refers to the broad process of finding knowledge in data, and emphasizes the high level application of particular data mining methods.
  • The goal of the KDD process is to extract knowledge from data in the context of large databases.

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  • The overall process of finding and interpreting patterns from data involves the repeated application of the following steps:
  1. Developing an understanding of:

    • The application domain
    • The relevant prior knowledge
    • The goals of end user
  2. Creating a target data set:

    • Selecting a data set or focusing on a subset of variables or data samples on which discovery is to be performed.
  3. Data cleaning and preprocessing:

    • Removal of noise or outliers.
    • Strategies for handling missing data fields.
  4. Data reduction and projection:

    • Finding useful features to represent the data depending on the goal of the task.
  5. Choosing the data mining task:

    • Deciding whether the goal of the KDD process is classification, regression, clustering, etc.
  6. Choosing the data mining algorithm:

    • Selecting methods to be used for searching the pattern in the data.
    • Deciding which models and parameters may be appropriate.
    • Matching a particular data mining method with the overall criteria of the KDD process.
  7. Data mining:

    • Searching for patterns of interest in a particular representational form or a set of such representations as classification rules or tress, regression, clustering, and so forth.
  8. Interpreting mined patterns

  9. Consolidating discovered knowledge

Architecture of Typical Data mining system

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  • Architecture of a typical data mining system may have the following major components as shown in fig:
  1. Database, data warehouse, or other information repository:

    • This is information repository.
    • Data cleaning and data integration techniques may be performed on the data.
  2. Databases or data warehouse server:

    • It fetches the data as per the users’ requirement which one need for data mining task.
  3. Knowledge base:

    • This is used to guide the search, and gives the interesting and hidden patterns from data.
  4. Data mining engine:

    • It performs the data mining task such as characterization, association, classification, cluster analysis etc.
  5. Pattern evaluation module:

    • It is integrated with the mining module and it give the search of only the interesting patterns.
  6. Graphical user interface:

    • This module is used to communicate between user and the data mining system and allow users to browse databases or data warehouse schemas.
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