• Shalini Bhaskar Bajaj
• Aman Jatain
• Priyanka Vashisht
• Ashima Narang

Pattern mining techniques play a crucial role in discovering meaningful associations and relationships in various application domains. Join-based algorithms are widely used for frequent and rare pattern mining tasks. In this paper, we present a comprehensive structural and empirical analysis of join-based techniques. We provide an overview of join-based algorithms, including their definition and concept, types, and strengths and limitations. Furthermore, we delve into the structural analysis of specific join-based algorithms, namely Apriori, Eclat, and FP-Join, highlighting their key components, candidate generation strategies, pruning techniques, and utilization of data structures. We also mention other notable join-based techniques such as LCM and PrefixSpan. To evaluate the performance of join-based algorithms, we set up a comprehensive experimental framework. We carefully choose benchmark datasets and performance metrics for both frequent pattern mining and rare pattern mining tasks. The analysis includes runtime, memory usage, scalability, and pattern quality evaluations. We present the results of the empirical analysis, providing insights into the performance and efficiency of join-based techniques in discovering frequent and rare patters. Additionally, we conduct a comparative analysis with tree-based techniques to highlight the strengths and limitations of join-based approaches. We discuss the suitability and applicability of join-based algorithms in specific scenarios and datasets, shedding light on their practical use. Furthermore, we identify potential research directions and advancements for join-based frequent and rare pattern mining techniques, considering emerging trends and technologies. Through this comprehensive analysis, we aim to provide researchers and practitioners with a deeper understanding of join-based algorithms, their performance characteristics, and their applicability in pattern mining tasks. The insights gained from this study can guide future research and foster advancements in join-based techniques, leading to more efficient and effective pattern mining solutions.

Join based algorithms;Apriori; Eclat; FP-Join

"A comprehensive structural and empirical analysis of Join based frequent and rare pattern mining techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p30-p34, May-2023, Available :http://www.jetir.org/papers/JETIR2305G05.pdf

"A comprehensive structural and empirical analysis of Join based frequent and rare pattern mining techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp30-p34, May-2023, Available at : http://www.jetir.org/papers/JETIR2305G05.pdf