A distributed frequent itemset mining algorithm using Spark for Big Data analytics

2015 ◽  
Vol 18 (4) ◽  
pp. 1493-1501 ◽  
Author(s):  
Feng Zhang ◽  
Min Liu ◽  
Feng Gui ◽  
Weiming Shen ◽  
Abdallah Shami ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Mining Algorithm

Data Mining Itemset of Big Data Using Pre-Processing Based on Mapreduce FrameWork with ETL Tools

2017 ◽  
Vol 2 (2) ◽  
pp. 57-62
Author(s):  
Padmanathan Anantharaman ◽  
H.V. Ramakrishan
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Hybrid Approach ◽  
Processing Technique ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Dataset Size

As data volumes continue to grow, they quickly consume the capacity of data warehouses and application databases. Is your IT organization forced into costly upgrades to expensive databases and data warehouse hardware appliances and enormous amount of data is getting explored through Internet of Things (IoT) as technologies are advancing and people uses these technologies in day to day activities, this data is termed as Big Data having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets but it has large communication cost which reduces execution efficiency. This proposed new pre-processed k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using k-means algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets from generated clusters using MapReduce programming model. Results shown that execution efficiency of ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as one of the pre-processing technique.

EAFIM: efficient apriori-based frequent itemset mining algorithm on Spark for big transactional data

2020 ◽  
Vol 62 (9) ◽  
pp. 3565-3583 ◽  
Author(s):  
Shashi Raj ◽  
Dharavath Ramesh ◽  
M. Sreenu ◽  
Krishan Kumar Sethi
Keyword(s):  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Mining Algorithm ◽  
Transactional Data

scholarly journals A Heuristic Rule Based Approximate Frequent Itemset Mining Algorithm

2016 ◽  
Vol 91 ◽  
pp. 324-333 ◽  
Author(s):  
Haifeng Li ◽  
Yuejin Zhang ◽  
Ning Zhang ◽  
Hengyue Jia
Keyword(s):  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Heuristic Rule ◽  
Rule Based ◽  
Itemset Mining ◽  
Mining Algorithm
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