# Data Mining project 1

###### tags: `Datamining`

* [X] Dataset1: Select from kaggle.com / UCI
* [X] Dataset2: Use IBM Quest Synthetic Data Generator
* [X] https://sourceforge.net/projects/ibmquestdatagen/
* [X] Generate different datasets
* [X] Frequent itemsets mining
* [X] Implement Apriori Algorithm and apply on these datasets
* [X] Hash?
* [ ] Tree?
* [X] FP-growth
* [X] Generate association rules
* [X] Compare your results

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### Dataset

* Kaggle [movielens](https://www.kaggle.com/jneupane12/movielens?select=ratings.csv)
* [IBM](https://github.com/StoneLin0708/DataMining2020/blob/master/data/ibm.csv)

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### Implement

* c++
* [GitHub](https://github.com/StoneLin0708/DataMining2020)

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### Mining Time - fp growth is much faster

* yellow-orange is apriori
* blue-green is fp growth

Confidence seems not to affect mining time as much as support is because frequent patterns are filtered by support before finding rules.



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### support & confidence

* support - how much sample
* confidence - pattern consistency

|support | confidence ||
|-|-|-|
|low|low|low frequency in dataset, do not have pattern|
|low|high|low frequency in dataset, but the pattern is consistent|
|high|low|high frequency in dataset, but it comes from many different subpatterns|
|high|high|high frequency in dataset and pattern is consistent|

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