---
title: "Data mining as a step in KDD"
subject: "Data Mining"
chapter: 1
key: "semester_7/data_mining:dm-as-kdd-step"
source: https://www.bctengineer.com/q/semester_7/data_mining/dm-as-kdd-step
site: BCT Engineering
---

# Data mining as a step in KDD

Data Mining · Chapter 1: Introduction

- Asked in: year not given
- 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/dm-as-kdd-step
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-1

## Question

Explain Data Mining as a step in KDD.

## Short answer

Data mining is the central step of KDD, applied after data cleaning, integration, selection and transformation. It involves choosing the mining task and algorithm and running it on prepared data to extract patterns. Pattern evaluation and knowledge presentation follow, and the process is iterative.

## Answer

**Knowledge discovery in databases (KDD)** is the overall process of turning raw data into useful knowledge. **Data mining is the central step of this process**, where intelligent methods are applied to extract patterns from the prepared data.

```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.

#### Position of data mining
- Steps 1 to 4 prepare the data (cleaning, integration, selection, transformation). Good preparation decides the quality of mining.
- **Step 5, data mining**: choose the task (classification, clustering, association, prediction), choose the algorithm (decision tree, k-means, Apriori) and run it on the transformed data to produce patterns.
- Steps 6 and 7 judge the patterns and present knowledge.
- The process is iterative: results of mining may lead back to earlier steps.

**Example**: for a telecom company, the data (call records) is cleaned, integrated with customer data, and selected for the last six months. Data mining then applies a decision-tree classifier to find which customers will leave. The rules are then evaluated, and the useful ones given to the marketing team.

## Check yourself

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

1. Where does data mining fit in KDD?
   A. It is the first step
   B. It is the central step (step 5) after preparation steps
   C. It is the last step
   D. It is not part of KDD
   Answer: B. Steps 1 to 4 prepare data, step 5 is mining, then evaluation and presentation. (Key point: Position of mining)

2. Why do steps 1 to 4 matter?
   A. They decide the quality of mining
   B. They are optional
   C. They produce the final knowledge
   D. They replace mining
   Answer: A. Good preparation decides the quality of the mined patterns. (Key point: Preparation importance)

3. What happens in the mining step?
   A. Choose a task and algorithm (e.g. decision tree, k-means, Apriori) and run it on transformed data
   B. Data is cleaned
   C. Patterns are visualised
   D. Sources are merged
   Answer: A. Task and algorithm are chosen and run to produce patterns. (Key point: Mining step actions)

## Related in Data Mining

- [KDD applied to WWW mining](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Akdd-in-www-mining) — Data Mining, chapter 7
- [Data mining and its applications](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-banking-biomedical) — Data Mining, chapter 1
- [Data mining vs DW and applications](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-vs-dw-applications) — Data Mining, chapter 1
- [CRM; security and privacy of data mining](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Acrm-security-privacy) — Data Mining, chapter 7
- [Where data mining is applicable](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-areas-applicable) — Data Mining, chapter 1
- [Descriptive vs predictive data mining](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-descriptive-predictive) — Data Mining, chapter 1

## Related in other subjects

- [Data warehouse and data mining](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-warehouse-data-mining) — Information System, chapter 4, asked 2×
- [Short note: data mining and warehousing](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-mining-and-warehousing-note) — Information System, chapter 4
- [Data mining and business knowledge](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-mining-business-knowledge) — Information System, chapter 4
- [IGES, STEP, DXF and STL data exchange](https://www.bctengineer.com/question.md?key=semester_4%2Fcomputer_aided_design%3Acad-data-exchange-formats) — Computer Aided Design, chapter 6