---
title: "Short note: KDD"
subject: "Data Mining"
chapter: 1
key: "semester_7/data_mining:kdd-short-note"
source: https://www.bctengineer.com/q/semester_7/data_mining/kdd-short-note
site: BCT Engineering
---

# Short note: KDD

Data Mining · Chapter 1: Introduction

- Asked in: year not given, 3 marks
- Video: [Data Mining & Business Intelligence | Tutorial #1 | The KDD Process](https://www.youtube.com/watch?v=U-hUnxuaw7w) — RANJI RAJ
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/kdd-short-note
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-1

## Question

Write a short note on 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."

## Answer

**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.

```text
 Raw data -> [1 Data cleaning] -> [2 Data integration]
          -> [3 Selection] -> [4 Transformation]
          -> [5 Data mining] -> [6 Pattern evaluation]
          -> [7 Knowledge presentation]
```
1. **Data cleaning**: remove noise and inconsistent data, fill missing values.
2. **Data integration**: combine data from several sources (databases, files, web) into one store.
3. **Data selection**: retrieve only the data relevant to the analysis task.
4. **Data transformation**: convert data into forms suitable for mining (normalisation, aggregation, generalisation).
5. **Data mining**: apply algorithms (classification, clustering, association) to extract patterns.
6. **Pattern evaluation**: identify the truly interesting patterns using measures such as support and confidence.
7. **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".

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. Which describes KDD best?
   A. A non-trivial, iterative process of finding valid, novel, useful and understandable patterns in large data
   B. A single algorithm for clustering
   C. A database storage format
   D. A way of backing up warehouses
   Answer: A. KDD is the iterative process of discovering valid, novel, potentially useful and understandable patterns. (Key point: Definition of KDD)

2. Which is the correct order of the KDD steps?
   A. Cleaning, selection, integration, mining, transformation, evaluation, presentation
   B. Cleaning, integration, selection, transformation, mining, evaluation, presentation
   C. Integration, cleaning, mining, transformation, selection, presentation, evaluation
   D. Selection, mining, cleaning, integration, transformation, evaluation, presentation
   Answer: B. The sequence is cleaning, integration, selection, transformation, data mining, pattern evaluation, knowledge presentation. (Key point: Order of KDD steps)

3. Approximately how much of the KDD effort goes to preprocessing (steps 1 to 4)?
   A. 5 to 10%
   B. 20 to 30%
   C. 60 to 80%
   D. Almost 100%
   Answer: C. Preprocessing takes about 60 to 80% of the effort. (Key point: Preprocessing effort)

4. What does it mean that KDD is interactive and iterative?
   A. Each step runs only once
   B. The user may return to earlier steps and refine
   C. Only the mining step can be repeated
   D. The process is fully automatic with no user
   Answer: B. Users can go back to earlier steps based on results. (Key point: Iterative nature)