IR-Eclat: A new algorithm of incremental R-Eclat for infrequent itemset mining.

The key challenge of association rule mining is to discover and extract a valuable information from databases. However, mining association rule may require repetitious scanning of large databases that leads to the high memory usage and affects the running time. Rare Equivalence Class Transformation (R-Eclat) algorithm is specifically design for infrequent itemset mining. In response to the promising results of mining in speedy processing time and taking consideration of dynamic transaction of data in a database, a new incremental approach is introduced called Incremental R-Eclat (IR-Eclat). This model adopted via relational database engine management system; My Structured Query Language (MySQL) serves as a database engine for testing benchmark datasets. The experimental results on several benchmark datasets indicate that Incremental R-Eclat outperforms the R-Eclat by reducing its running time to process especially in dynamic database as the data is increasing in volume from time to time. [ABSTRACT FROM AUTHOR]