---
title: "Processes in data preprocessing"
subject: "Data Mining"
chapter: 2
key: "semester_7/data_mining:preprocessing-processes"
source: https://www.bctengineer.com/q/semester_7/data_mining/preprocessing-processes
site: BCT Engineering
---

# Processes in data preprocessing

Data Mining · Chapter 2: Data Pre-processing

- Asked in: year not given
- Video: [Data Mining | Data pre-processing (data cleaning, integration, reduction, transformation)](https://www.youtube.com/watch?v=olZGR0bL6XM) — Constant Learners
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/preprocessing-processes
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-2

## Question

Explain different processes involved in data preprocessing.

## Short answer

Data preprocessing converts raw data into clean data for mining. Processes: cleaning (fill missing, smooth noise, remove outliers), integration (combine sources, resolve conflicts), transformation (normalization, aggregation, attribute construction), reduction (dimensionality, numerosity, compression), discretization (continuous to intervals). Steps repeat as needed before mining.

## Answer

**Data preprocessing** is the set of steps that turn raw, dirty data into clean, consistent data that mining algorithms can use. Real data is usually incomplete, noisy and inconsistent, so poor data gives poor patterns ("garbage in, garbage out").

#### Major processes
1. **Data cleaning**: fill missing values, smooth noisy data (binning, regression, clustering), detect and remove outliers, and correct inconsistencies.
2. **Data integration**: combine data from several sources (databases, files, data cubes) into one store. It resolves entity identification, redundant attributes (found by correlation analysis) and conflicting values.
3. **Data transformation**: convert data into a form suitable for mining.
   - Normalization (min-max, z-score, decimal scaling)
   - Aggregation and generalization (day to month, city to country)
   - Attribute construction and smoothing
4. **Data reduction**: get a smaller representation that gives nearly the same results.
   - Dimensionality reduction (PCA, wavelets, feature selection)
   - Numerosity reduction (sampling, histograms, clustering)
   - Data compression
5. **Data discretization**: replace continuous values by intervals or concept labels (e.g. age 0-18, 19-60, 60+). It is also a form of reduction.

```text
Raw data -> Cleaning -> Integration -> Transformation
         -> Reduction -> Discretization -> Clean data
```

These steps are not strictly in sequence; they are repeated as needed before mining.

## Check yourself

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

1. Why is data preprocessing needed?
   A. Mining algorithms cannot read numbers
   B. Real data is incomplete, noisy and inconsistent, and poor data gives poor patterns
   C. It always increases data volume
   D. It replaces mining
   Answer: B. Garbage in, garbage out: cleaning raw data improves mining results. (Key point: Why preprocess)

2. What does data integration resolve?
   A. Missing values by regression
   B. Choosing the number of bins
   C. Entity identification, redundant attributes and conflicting values from several sources
   D. Only the choice of sampling method
   Answer: C. Integration combines multiple sources and resolves redundancy and conflicts. (Key point: Data integration)

3. Which belongs to data reduction?
   A. Filling missing values
   B. Min-max normalisation
   C. Correlation analysis for merging sources
   D. Dimensionality reduction, numerosity reduction and compression
   Answer: D. Reduction gives a smaller representation with nearly the same results. (Key point: Data reduction)

4. What does discretization do?
   A. Replaces continuous values by intervals or concept labels, e.g. age 0-18, 19-60, 60+
   B. Replaces categories by real numbers
   C. Removes duplicate tuples
   D. Fits a regression line
   Answer: A. Continuous values become intervals; it is also a form of reduction. (Key point: Discretization)

## Related in Data Mining

- [Data preprocessing and handling noisy data](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Apreprocessing-noisy-data) — Data Mining, chapter 2
- [Short note: Data preprocessing](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adata-preprocessing-note) — Data Mining, chapter 2
- [Preprocessing steps, binning, normalization](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Apreprocessing-binning-normalization) — Data Mining, chapter 2