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KDD

2 min read

KDD

The KDD process stands for Knowledge Discovery in Databases, a methodology used in data mining to extract useful knowledge from large datasets. It’s a structured approach to identifying patterns, trends, and insights from raw data. Introduced by Fayyad et al. in 1996, it’s widely used in fields like machine learning, business intelligence, and scientific research. Here’s a breakdown of the steps:

Selection

  • Choosing the relevant data from a larger dataset or multiple sources to focus on for analysis.
  • Not all data is useful—selecting the right subset ensures efficiency and relevance.
  • If you’re analyzing customer behavior, you might select only purchase history and demographics from a massive company database.

Preprocessing

  • Cleaning and preparing the selected data by handling missing values, removing noise, and resolving inconsistencies.
  • Real-world data is often messy (e.g., typos, duplicates, or incomplete entries), and this step improves quality.
  • Filling in missing ages with an average or removing outlier transactions that don’t make sense (like a $1M purchase from a toddler).

Transformation

  • Converting the preprocessed data into a suitable format for analysis, often by reducing dimensionality, normalizing, or aggregating it.
  • Makes the data easier to work with for algorithms or tools (e.g., turning text into numerical values).
  • Converting sales dates into “month of sale” or scaling income values to a 0–1 range.

Data Mining

  • Applying algorithms to uncover patterns, relationships, or anomalies in the transformed data.
  • This is the core step where insights are generated—think clustering, classification, or association rules.
  • Using a clustering algorithm to group customers by spending habits or a decision tree to predict churn.

Interpretation/Evaluation

  • Analyzing the mined patterns to determine their usefulness, validity, and relevance, often visualizing or testing them.
  • Not all patterns are meaningful—interpretation ensures the results align with goals and aren’t just noise.
  • Checking if a discovered trend (e.g., “younger customers buy more on weekends”) holds up across regions or makes business sense.