---
title: "Apriori advantages and disadvantages"
subject: "Data Mining"
chapter: 4
key: "semester_7/data_mining:apriori-pros-cons"
source: https://www.bctengineer.com/q/semester_7/data_mining/apriori-pros-cons
site: BCT Engineering
---

# Apriori advantages and disadvantages

Data Mining · Chapter 4: Association Analysis

- Asked in: year not given
- Video: [Lec - 18: Apriori Algorithm in Data Mining | Real Life Example](https://www.youtube.com/watch?v=o_hnNBM0jtk) — Gate Smashers
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/apriori-pros-cons
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-4

## Question

Discuss about advantages and disadvantages of apriori algorithm.

## Short answer

Advantages: simple, uses Apriori property to prune search, parallelizable, complete results. Disadvantages: multiple database scans, huge candidate generation, high computation/memory, slow at low support/dense data, requires user-set thresholds, treats items equally, many redundant rules. FP-growth avoids these issues.

## Answer

#### Advantages
- **Simple and easy** to understand and implement.
- Uses the **Apriori property** (all subsets of a frequent itemset are frequent) to prune the search space heavily, so it is far better than testing all 2ⁿ itemsets.
- Easy to **parallelise** (candidate counting is independent) and works well with sparse data and a high minimum support.
- Produces results that are complete (all frequent itemsets are found).
- Works on large transaction databases, and the many variations (hashing, partitioning, sampling) can improve it.

#### Disadvantages
- **Multiple database scans**: one for each itemset size k, which is costly for large databases.
- **Large number of candidates**: for 10⁴ frequent 1-itemsets, more than 10⁷ candidate 2-itemsets are generated, and very long patterns need exponential candidates.
- **High computation and memory** for candidate generation, and for subset testing against every transaction.
- Becomes very slow with **low minimum support** or **dense data** with long frequent patterns.
- Needs min_sup and min_conf to be set by the user; wrong values give too few or too many rules.
- Treats all items equally (no weights or quantity), and many generated rules may be uninteresting or redundant.

FP-growth overcomes the candidate generation and repeated scanning problems.

## Check yourself

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

1. Why is Apriori far better than testing all 2^n itemsets?
   A. The Apriori property prunes the search space heavily
   B. It needs no minimum support
   C. It scans the database only once
   D. It never generates candidates
   Answer: A. Subsets-of-frequent-are-frequent lets it discard many candidates without counting. (Key point: Main advantage)

2. Which is a disadvantage of Apriori regarding scans?
   A. It needs only two scans, which loses accuracy
   B. One scan per itemset size k, costly for large databases
   C. It cannot be parallelised
   D. It never finds long patterns
   Answer: B. Repeated scans are expensive. (Key point: Database scans)

3. When does Apriori become very slow?
   A. With high minimum support on sparse data
   B. With few transactions
   C. With low minimum support or dense data with long patterns
   D. When items are weighted
   Answer: C. Low support or dense data gives many frequent items and exponential candidates. (Key point: Condition of weakness)

4. Which algorithm overcomes Apriori's candidate generation and repeated scanning?
   A. K-means
   B. Naive Bayes
   C. DBSCAN
   D. FP-growth
   Answer: D. FP-growth builds an FP-tree and avoids candidate generation. (Key point: Remedy)

## Related in Data Mining

- [Association analysis and Apriori pruning](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aassociation-apriori-pruning) — Data Mining, chapter 4, asked 2×
- [Apriori on 8 transactions, 25% support](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aapriori-b65-transactions) — Data Mining, chapter 4
- [Apriori on four transactions, support 2](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aapriori-y63-min-support-2) — Data Mining, chapter 4
- [Short note: Apriori and its drawbacks](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aapriori-note-drawbacks) — Data Mining, chapter 4
- [FP-Growth and Apriori disadvantage](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Afp-growth-vs-apriori) — Data Mining, chapter 4
- [Apriori on HotDogs data with rules](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Aapriori-hotdogs-rules) — Data Mining, chapter 4