A Comparative Study on Association Rule Mining in Distributed Data Mining

Association rule mining (ARM) is a data mining approach used to identify interesting relationships and patterns in massive datasets. Its objective is to categories associations between various items or variables based on their co-occurrence in the dataset. The fundamental idea behind ARM is to find hidden patterns that can provide important information for prediction, decision-making, and other data analysis tasks. With the rise of big data and the proliferation of distributed computing platforms, there is a need to investigate and compare various methods for doing association rule mining in remote datasets. This paper provides a complete comparative analysis of ARM algorithms and methodologies in distributed systems. We examine the scalability, performance, and efficiency of many cutting-edge algorithms, emphasizing their strengths, limits, and applicability to various types of distributed datasets. This work also provides insights into the present landscape of association rule mining in remote datasets, as well as help researchers and practitioners choose the best algorithms for their particular needs.