---
title: "DM techniques; likelihood and distance principle"
subject: "Data Mining"
chapter: 3
key: "semester_7/data_mining:dm-techniques-close-data-alike"
source: https://www.bctengineer.com/q/semester_7/data_mining/dm-techniques-close-data-alike
site: BCT Engineering
---

# DM techniques; likelihood and distance principle

Data Mining · Chapter 3: Classification

- Asked in: year not given, 2+6 marks; year not given, 3+5 marks
- Video: [All Major Data Mining Techniques Explained With Examples](https://www.youtube.com/watch?v=dUm3ptTQr0Q) — Learn with Whiteboard
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/dm-techniques-close-data-alike
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-3

## Question

What are the different techniques used in data mining? Explain likelihood and distance principle ("Data that are close to each other are very alike") with suitable supporting example.

## Short answer

Data mining techniques include classification, prediction or regression, clustering, association rule mining, sequential pattern mining, anomaly detection, summarisation and time-series analysis. Likelihood and distance principles mean nearby objects in attribute space are alike, so nearest neighbours or highest-likelihood groups decide classification or clustering. Example: customer X at (26,42) is nearest to two laptop buyers, so X is predicted yes and clustered with them.

## Answer

#### Data mining techniques
1. **Classification**: assign objects to predefined classes using a model learnt from labelled data (decision tree, Naive Bayes, KNN, neural network).
2. **Prediction/regression**: estimate a continuous value.
3. **Clustering**: group unlabelled objects into natural clusters (k-means, hierarchical, DBSCAN).
4. **Association rule mining**: find items that occur together (Apriori, FP-growth), e.g. {bread} → {butter}.
5. **Sequential pattern mining**: frequent ordered events over time.
6. **Anomaly (outlier) detection**: find unusual objects, e.g. fraud.
7. **Summarisation/characterisation and discrimination**: concise description of a data set and comparison of classes.
8. **Time-series and trend analysis**.

#### Likelihood and distance principle
"Data that are close to each other are very alike": the distance between objects in attribute space tells how similar they are, and objects near to each other probably have the same class or belong to the same cluster. The likelihood part chooses the most probable group for a new object, given its neighbours.

**Example**: customers described by (age, income in thousand rupees).

| Customer | Age | Income | Buys laptop |
| --- | --- | --- | --- |
| C1 | 25 | 40 | yes |
| C2 | 27 | 45 | yes |
| C3 | 55 | 90 | no |

New customer X = (26, 42). d(X, C1) = √(1 + 4) = 2.24, d(X, C2) = √(1 + 9) = 3.16, d(X, C3) = √(841 + 2304) = 56.1. The two nearest neighbours buy laptops, so X is predicted "yes". The same principle puts C1, C2 and X in one cluster and C3 in another.

## Check yourself

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

1. Which technique finds items that occur together, e.g. {bread} -> {butter}?
   A. Clustering
   B. Classification
   C. Association rule mining
   D. Anomaly detection
   Answer: C. Apriori and FP-growth are examples. (Key point: Association rule mining)

2. What does 'data that are close to each other are very alike' imply?
   A. Nearby objects must be outliers
   B. Distance is irrelevant
   C. Nearby objects have different classes
   D. Nearby objects probably share the same class or cluster
   Answer: D. Distance in attribute space reflects similarity. (Key point: Principle)

3. For X = (26,42) and C1 = (25,40), the Euclidean distance is:
   A. sqrt(5) = 2.24
   B. sqrt(9) = 3
   C. sqrt(10) = 3.16
   D. sqrt(3145) = 56.1
   Answer: A. sqrt(1^2 + 2^2) = sqrt(5). (Key point: Worked example)

4. Why is X predicted 'yes' for buying a laptop?
   A. C3 is farthest
   B. Its two nearest neighbours C1 and C2 buy laptops
   C. Its age is the median
   D. Income is highest
   Answer: B. C3, who does not buy, is far away. (Key point: Prediction)

## Related in Data Mining

- [Short note: Likelihood and distance](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Alikelihood-distance-note) — Data Mining, chapter 3
- [Likelihood and distance; K-means drawbacks](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Alikelihood-distance-clustering-kmeans-drawbacks) — Data Mining, chapter 3
- [Web mining, importance and techniques](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aweb-mining-importance-techniques) — 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

## Related in other subjects

- [Data mining and business knowledge](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-mining-business-knowledge) — Information System, chapter 4
- [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 transfer techniques for slow I/O](https://www.bctengineer.com/question.md?key=semester_5%2Finstrumentation_ii%3Aio-speed-mismatch) — Instrumentation II, chapter 1, asked 3×