D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

D-GENE: Deferring the GENEration of Power Sets for Discovering Frequent Itemsets in Sparse Big Data

Sparseness is the distinctive aspect of big data generated by numerous applications at present. Furthermore, several similar records exist in real-world sparse datasets. Based on Iterative Trimmed Transaction Lattice (ITTL), the recently proposed TRICE algorithm learns frequent itemsets efficiently...

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Bibliographic Details
Main Authors: Muhammad Yasir, Muhammad Asif Habib, Muhammad Ashraf, Shahzad Sarwar, Muhammad Umar Chaudhry, Hamayoun Shahwani, Mudassar Ahmad, CH. Muhammad Nadeem Faisal
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Big data applications
pattern recognition
association rules
frequent item set mining
IoT
Online Access:https://ieeexplore.ieee.org/document/8984308/

Internet

https://ieeexplore.ieee.org/document/8984308/

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