Abstract
Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. Such patterns reflect frequent and infrequent patterns. An abundant literature has dedicated in frequent pattern mining and tremendous efficient algorithms for frequent itemset mining in the transaction database. Nonetheless, the infrequent pattern mining has emerged to be an interesting issue in discovering patterns that rarely occur in the transaction database. More researchers reckon that rare pattern occurrences may offer valuable information in knowledge data discovery process. The R-Eclat is a novel algorithm that determines infrequent patterns in the transaction database. The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. This paper proposes IF-Postdiffset as a new variant in R-Eclat algorithm. This paper also highlights the performance of infrequent mining pattern from the transaction database among different variants of the R-Eclat algorithm regarding its execution time.
Highlights
With the emergence of big data transformation, companies cannot store all their records for long durations and inefficiently manage huge datasets
The R-Eclat algorithm is an innovative technique that is specially devised for infrequent pattern mining ([14, 15])
Itemset mining is a dynamic field of research in association rules
Summary
With the emergence of big data transformation, companies cannot store all their records for long durations and inefficiently manage huge datasets. Past technologies have limited storage capacity, rigid, and expensive management tools Their lack of scalability, flexibility, and performance needs to be addressed within the big data context. The management of big data requires important resources, new methods, and powerful technologies. Association rule mining (ARM) ([3,4]) plays an essential role in mining association and correlations among itemsets in the dataset. The ARM determines association rules that satisfy predefined minimum support and confidence from a given database. Support indicates the frequency of pattern, while confidence specifies the strength of rule implication. Two major patterns can be found in the datasets which are frequent and infrequent itemsets. The ARM algorithms extract frequent itemsets to display high frequency/support in the transaction database.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
