Survey on the current status of serial and parallel algorithms of frequent intemset mining

• Main Authors: Uy, Roger Luis, Suarez, Merlin Teodosia C.
• Format: text
• Published: Animo Repository 2015
• Subjects: Data mining; Electronic data processing—Distributed processing; Parallel algorithms; Computer Engineering; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/12623
• Institution: De La Salle University

Frequent itemset mining is one of the fundamental but time-demanding tasks in data mining. It is used to find frequent patterns and generate association rules for these patterns. With the availability of inexpensive storage and progress in data capture technology, the volume of data has reached exa-scale level. But improvements in processor and network technology opened up opportunities for parallel and distributed computing to be applied in frequent itemset mining to enhance its performance in the light of big data. Thus, there are challenges in frequent itemset mining to fully harness the parallel computing capability of the computer hardware technologies. This paper reviews the development of current serial and parallel approaches to frequent itemset mining and discusses future research directions in this field.