An association rule learning application that uses Apriori algorithm
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This project made as a class assignment.It's purpose basically found some association rules in given input.
Features:
- You can find association rules on any given data in csv format
- You can filter it with minimum support and three different second measurement types
- You can see how many rules found and each rules measurement values
- You can see list of frequent subsets
Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. The Apriori algorithm was proposed by Agrawal and Srikant in 1994. Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation or IP addresses)
To get a local copy up and running follow these simple steps.
- Clone the repo
git clone https://github.com/izzettunc/apriori.git-
Open Aprirori.sln with Visual Studio
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Make changes, run it, use it whatever you like 😄
Here is some unneeded screenshots for how to use it
See the open issues for a list of proposed features (and known issues).
Distributed under the MIT License. See LICENSE for more information.
İzzet Tunç - izzet.tunc1997@gmail.com
