scholarly journals Data Partitioning for Fast Mining of Frequent Itemsets in Massively Distributed Environments

2015 ◽  
pp. 303-318 ◽  
Author(s):  
Saber Salah ◽  
Reza Akbarinia ◽  
Florent Masseglia
Keyword(s):  
Frequent Itemsets ◽  
Data Partitioning ◽  
Distributed Environments

scholarly journals Data placement in massively distributed environments for fast parallel mining of frequent itemsets

2017 ◽  
Vol 53 (1) ◽  
pp. 207-237 ◽  
Author(s):  
Saber Salah ◽  
Reza Akbarinia ◽  
Florent Masseglia
Keyword(s):  
Data Placement ◽  
Frequent Itemsets ◽  
Distributed Environments ◽  
Parallel Mining

Frequent itemsets grouping algorithm based on Hash list

2013 ◽  
Vol 33 (11) ◽  
pp. 3045-3048
Author(s):  
Hongmei WANG ◽  
Ming HU
Keyword(s):  
Frequent Itemsets ◽  
Grouping Algorithm

scholarly journals A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream

2021 ◽  
Vol 16 (2) ◽  
pp. 1-30
Author(s):  
Guangtao Wang ◽  
Gao Cong ◽  
Ying Zhang ◽  
Zhen Hai ◽  
Jieping Ye
Keyword(s):  
Frequency Estimation ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Experimental Results ◽  
Closure Property ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Minimum Value ◽  
Downward Closure ◽  
Bounded Size

The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.

scholarly journals Evaluating Worksharing Tasks on Distributed Environments

2020 ◽  
Author(s):  
Marcos Maronas ◽  
Xavier Teruel ◽  
J. Mark Bull ◽  
Eduard Ayguade ◽  
Vicenc Beltran
Keyword(s):  
Distributed Environments
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