- Year not given · 3 marks
Short note: KDD
Short answer
KDD is the iterative process of discovering valid, novel, useful patterns from large data. It includes cleaning, integration, selection, transformation, data mining, pattern evaluation, and presentation. Preprocessing consumes 60–80% of the effort. Example: a bank finds a rule like "age < 25 and no collateral → high default risk."
Write a short note on KDD.
KDD (Knowledge Discovery in Databases) is the non-trivial, iterative process of identifying valid, novel, potentially useful and understandable patterns in large data. Data mining is the core step of KDD.
Raw data -> [1 Data cleaning] -> [2 Data integration]
-> [3 Selection] -> [4 Transformation]
-> [5 Data mining] -> [6 Pattern evaluation]
-> [7 Knowledge presentation]
- Data cleaning: remove noise and inconsistent data, fill missing values.
- Data integration: combine data from several sources (databases, files, web) into one store.
- Data selection: retrieve only the data relevant to the analysis task.
- Data transformation: convert data into forms suitable for mining (normalisation, aggregation, generalisation).
- Data mining: apply algorithms (classification, clustering, association) to extract patterns.
- Pattern evaluation: identify the truly interesting patterns using measures such as support and confidence.
- Knowledge presentation: show the knowledge to the user with visualisation and reports.
Key points
- The process is interactive and iterative: the user may return to earlier steps.
- Preprocessing (steps 1 to 4) takes most of the effort, about 60 to 80%.
- Input: raw data in databases or warehouse; output: knowledge such as rules, clusters and classification models.
Example: a bank applies KDD on loan data to find a rule such as "age < 25 and no collateral → high default risk".
✓ Answer checked by Claude Opus 5.5 (high effort)