Knowledge Discovery in Databases (KDD)
- A multidisciplinary process for extracting useful patterns and information from data sets.
- Typical workflow includes data cleaning/preprocessing, feature selection/transformation, model building/evaluation, and interpreting results.
- Applied in business (e.g., market segmentation, targeted promotions) and scientific research to improve decisions and uncover patterns.
Definition
Section titled “Definition”Knowledge discovery in databases (KDD) is the process of discovering useful information and patterns in data sets. It is a multidisciplinary field that uses techniques from statistics, machine learning, and data mining to extract knowledge from data. KDD involves several steps, including data cleaning and preprocessing, feature selection and transformation, model building and evaluation, and interpretation of results.
Explanation
Section titled “Explanation”KDD combines methods from statistics, machine learning, and data mining to extract actionable knowledge from large or complex data sets. The process is structured into stages: preparing and cleaning the data, selecting and transforming features, building and evaluating models, and interpreting the results. The outcomes of KDD can guide decision making, optimize business processes, and reveal relationships or patterns that may be difficult to detect with traditional statistical approaches.
Examples
Section titled “Examples”Clustering for market segmentation
Section titled “Clustering for market segmentation”Using clustering algorithms to group similar data points can help identify different groups of consumers with similar characteristics, which allows a company to tailor its marketing efforts.
Association rule mining for co-purchases
Section titled “Association rule mining for co-purchases”Association rule mining can reveal relationships between variables in a data set. For example, a retailer could discover that customers who purchase item A are also likely to purchase item B, and use that information for targeted promotions.
Use cases
Section titled “Use cases”- Businesses and organizations: improve decision making, optimize business processes, and gain a competitive edge.
- Scientific research: uncover patterns and relationships in data that are difficult to discover using traditional statistical methods.
Related terms
Section titled “Related terms”- Statistics
- Machine learning
- Data mining
- Clustering
- Association rule mining
- Market segmentation