In frequent itemset mining, the main challenge is to discover relationships between data in a transactional database or relational database. Various algorithms have been introduced to process frequent itemset. Eclat based algorithms are one of the prominent algorithm used for frequent itemset mining. Various researches have been conducted based on Eclat based algorithm such as Tidset, dEclat, Sortdiffset and Postdiffset. The algorithm has been improvised along the time. However, the utilization of physical memory and processing time become the main problem in this process. This paper reviews and presents a comparison of various Eclat based algorithms for frequent itemset mining and propose an enhancement technique of Eclat based algorithm to reduce processing time and memory usage. The experimental result shows some improvement in processing time and memory utilization in frequent itemset mining.
Collaboration network of the authors
9 authors ·
17 co-authorship links
· 3 across different organisations
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
Universiti Malaysia Terengganu
University of Technology Malaysia
Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.