Association Rule Mining Using an Unsupervised Neural Network with an Optimized Genetic Algorithm Peddi Kishor and Porika Sammulal Abstract The best known and most widely utilized pattern finding algorithm in data mining applications is association rule mining (ARM). Extraction of frequent patterns is an indispensable step in ARM. Most studies in the literature have been implemented on the concept of support and confidence framework utilization. Here, we investigated an ef ficient and robust ARM scheme based on a self-organizing map (SOM) and an optimized genetic algorithm (OGA). A SOM is an unsupervised neural network that ef ficaciously produces spatially coordinated internal feature representations and detected abstractions in the input space and is the most ef ficient clustering technique that reveals conventional similarities in the input space by performing a topology maintaining mapping. Hence, a SOM is utilized to generate accurate clustered frequency patterns and an OGA is used to generate positive and negative association rules with multiple consequences by studying all possible patterns. Experimental analysis on various datasets has shown the robustness of our proposed ARM in comparison to traditional rule mining approaches by proving that a greater number of positive and negative association rules is generated by the proposed methodology resulting in a better performance when compared to con- ventional rule mining schemes. Keywords Data mining ⋅ Association rule mining ⋅ Frequent patterns Positive and negative association rules ⋅ Self-organizing maps (SOM) Optimized genetic algorithm (OGA) P. Kishor ( ✉ ) R&D Cell, JNTUH, Hyderabad, India e-mail: kishorpeddi25@gmail.com P. Sammulal Department of CSE, JNTUH College of Engineering, Jagtial, India © Springer Nature Singapore Pte Ltd. 2019 A. Kumar and S. Mozar (eds.), ICCCE 2018, Lecture Notes in Electrical Engineering 500, https://doi.org/10.1007/978-981-13-0212-1_67 657