> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/machine-learning/data-mining.md).

# Data Mining

### **ASSOCIATION RULES**

1. [**Association rules slides**](https://www.slideshare.net/wanaezwani/apriori-and-eclat-algorithm-in-association-rule-mining) **- apriori, eclat, fp growth - pretty complete**
2. [**Terms**](https://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html) **- lift, confidence**
3. [**Paper - basic concepts and algo**](https://www-users.cs.umn.edu/~kumar001/dmbook/ch5_association_analysis.pdf)

**Knoldus**

1. [**Apriori**](https://blog.knoldus.com/machinex-why-no-one-uses-apriori-algorithm-for-association-rule-learning/)
2. [**Association rules**](https://blog.knoldus.com/machinex-two-parts-of-association-rule-learning/)
3. [**Fp-growth**](https://blog.knoldus.com/machinex-frequent-itemset-generation-with-the-fp-growth-algorithm/)
4. [**Fp-tree construction**](https://blog.knoldus.com/machinex-understanding-fp-tree-construction/)

**APRIORI** 

1. [**Apyori tut**](https://stackabuse.com/association-rule-mining-via-apriori-algorithm-in-python/) [**git**](https://github.com/ymoch/apyori)
2. [**Efficient apriori**](https://github.com/tommyod/Efficient-Apriori)
3. [**One of the best known association rules algorithm**](https://machinelearningmastery.com/market-basket-analysis-with-association-rule-learning/) **- apriori in weka**
4. [**A very good visual example of a transaction DB with the apriori algorithm step by step**](http://www.lessons2all.com/Apriori.php)
5. [**Python 3.0 code**](http://adataanalyst.com/machine-learning/apriori-algorithm-python-3-0/)
6. [**Mlxtnd**](http://rasbt.github.io/mlxtend/api_subpackages/mlxtend.frequent_patterns/) [**tutorial**](https://www.geeksforgeeks.org/implementing-apriori-algorithm-in-python/)
   1. **Apriori**
   2. **Rules**
   3. **pgrowth**
   4. **fpmax**

**FP Growth**

1. [**How to construct the fp-tree**](https://www.youtube.com/watch?v=gq6nKbye648)
2. **The same example, but with a graph that shows that lower support cost less for fp-growth in terms of calc time.**
3. [**Coursera video**](https://www.coursera.org/learn/data-patterns/lecture/ugqCs/2-5-fpgrowth-a-pattern-growth-approach)
4. **Another clip video**
5. [**How to validate these algorithms**](https://stackoverflow.com/questions/32843093/how-to-validate-association-rules) **- probably the best way is confidence/support/lift**

**It depends on your task. But usually you want all** [**three to be high.**](https://stats.stackexchange.com/questions/229523/association-rules-support-confidence-and-lift)

* **high support: should apply to a large amount of cases**
* **high confidence: should be correct often**
* **high lift: indicates it is not just a coincidence**

1. [**Difference between apriori and fp-growth**](https://www.quora.com/What-is-the-difference-between-FPgrowth-and-Apriori-algorithms-in-terms-of-results)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://www.mlcompendium.com/machine-learning/data-mining.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.