- System developed in Java that consists of Weka API to display association rules.
- Association rules are if-then statements that help to show the probability of relationships between data items within large data sets in various types of databases. Association rule mining has a number of applications and is widely used to help discover sales correlations in transactional data or in medical data sets.
- In this system, the user can choose a file to calculate the best membership rules within minutes.
- The Weka API uses the apriori algorithm. 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.
- Developed in 2018 for scientific initiation.
- Java
- Swing
- Weka API
- Name: Thiago Henrique Viotto
- University graduate: Systems Analysis in Unicamp
- Mail: thiago.henriqueviotto1@gmail.com