• Sasikumar P
• KT Meena Abarna

Association rule mining has garnered widespread adoption within the data mining community, facilitating the discovery of relationships among frequently co-occurring item-sets. Alongside the exploration of positive association rules, the mining of negative association rules, aimed at identifying negation relationships among frequent item-sets, assumes equal significance. Notably, negative association rule mining plays a crucial role in customer-driven domains, such as market basket analysis, where the identification of conflicting products holds practical value. In the context of multi-database mining, the extraction of negation relationships among item-sets and the synthesis of global negative association rules from multiple geographically dispersed data sources become pivotal in shaping strategic and branch-level decisions. This research endeavor aims to propose a method for synthesizing global negative association rules, which are collectively endorsed by a majority of participating data sources during the mining process across diverse data repositories. To validate the theoretical foundation of the proposed approach, experimental data from the UCI machine learning repository data set are employed for rigorous testing. The investigation includes an in-depth analysis of space and time complexity, demonstrating the efficiency and efficacy of the proposed methodology.

"Synthesis and Application of Global Negative Association Rules in Multi-Database Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.h187-h194, July-2022, Available :http://www.jetir.org/papers/JETIR2207731.pdf

"Synthesis and Application of Global Negative Association Rules in Multi-Database Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. pph187-h194, July-2022, Available at : http://www.jetir.org/papers/JETIR2207731.pdf