Frequent Itemset Mining for Big Data Using Greatest Common Divisor Technique
The discovery of frequent itemsets is one of the very important topics in data mining. Frequent itemset discovery techniques help in generating qualitative knowledge which gives business insight and helps the decision makers. In the Big Data era the need for a customizable algorithm to work with big...
Main Authors: | Mohamed A. Gawwad, Mona F. Ahmed, Magda B. Fayek |
---|---|
Format: | Article |
Language: | English |
Published: |
Ubiquity Press
2017-05-01
|
Series: | Data Science Journal |
Subjects: | |
Online Access: | http://datascience.codata.org/articles/686 |
Similar Items
-
A Fast Approach for Up-Scaling Frequent Itemsets
by: Runzi Chen, et al.
Published: (2020-01-01) -
TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data
by: Muhammad Yasir, et al.
Published: (2019-01-01) -
IIS-Mine: A new efficient method for mining frequent itemsets
by: Supatra Sahaphong
Published: (2012-04-01) -
Efficient Frequent Closed Itemset Algorithms With Applications To Stream Mining And Classification
by: Ranganath, B N
Published: (2010) -
FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining
by: Tianyou Wei, et al.
Published: (2020-01-01)