An Improved Eclat Algorithm for Mining Association Rules Based on Increased Search Strategy

Ma, Zhongqing; Yang, Juncheng; Zhang, Taixia; Fan, Liu

doi:10.14257/ijdta.2016.9.5.26

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…Eclat-Growth algorithm is proposed by Zhiyong Ma et al in 2016 [18]. Eclat-Growth is designed based on increased search strategy.…”

Section: A Eclat Growthmentioning

confidence: 99%

Multi-Core Processing Cloud Eclat Growth

Priya*,

Murugan

2019

IJEAT

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Data mining is a lively process used in many leading technologies of this information era. Eclat growth is one of the best performance data mining algorithms. This work is indented to create a suave interface for Eclat growth algorithm to run in multi-core processor-based cloud computing environments. Recent improvements in processor manufacturing technology make it possible to create multi-core high performance Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Many cloud services are already providing accessibility to these high-power processor virtual machines. The process of blending these technologies with Eclat Growth is proposed here in the name of “Multi-core Processing Cloud Eclat Growth” (MPCEG) to achieve higher processing speeds without compromising the standard data mining metrics such as Accuracy, Precision, Recall and F1-Score. New procedures for Cloud Parallel Processing, GPU Utilization, Annihilation of floating point arithmetic errors by fixed point replacement in GPUs and Hierarchical offloading aggregation are introduced in the construction process of proposed MPCEG

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“…Eclat-Growth algorithm is proposed by Zhiyong Ma et al in 2016 [18]. Eclat-Growth is designed based on increased search strategy.…”

Section: A Eclat Growthmentioning

confidence: 99%

Multi-Core Processing Cloud Eclat Growth

Priya*,

Murugan

2019

IJEAT

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“…This algorithm involves recursive techniques so that it saves more RAM and this is confirmed, [23] that the recursive technique in this method is useful for reducing complexity and in the process begins from the frequent 2-itemset. [24] the principal of this method is different from apriori algorithm, if the apriori method uses a horizontal format then the eclat method uses a vertical format. The horizontal format is described as TID-Item, while the vertical format is described as Item-TIDset.…”

Section: Eclat Algorithmmentioning

confidence: 99%

Data Pattern Of Computer Maintenance Management System With Eclat Algorithm

Sukmana

Rozi

2019

KINETIK

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Decision support system, basically used to help choosing some solution for stakeholde to take the best decision in manufacturer. In manufacturer company using Enterprise Resource System (ERP) that has Work Oder (WO) modul as request maintenance from user. But many of data from WO still didn’t use to help decision making and only as warehouse data about infrastructure maintenance from last time. Because that, author use that data to help technician to take decision making by using association rule as pattern processing. This is because WO has unique pattern that has problem (p), symptom (s), and root cause (r). Previous research (Sukmana, Rozi, 2017) was proved if association rule can use to help people to take decison making, it is just involved two variable, that is symptom (s), root cause (r) and using apriori algorithm as association rule. And focussing in this research is using that three variable and eclat algorithm as association rule methode. Result of this research has purpose to take the best pattern when using eclat algorithm.

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“…The Apriori [1], FP-Growth [3], Eclat [5] and K-Apriori [6] are the most widely used data mining algorithms for frequent itemstes mining as well as association rules generation. The Apriori algorithm is the popular and first developed algorithm for frequent pattern mining, but the main limitation is, it requires multiple scanning of transaction datasets and generates huge number of candidate itemsets that reduce the efficiency of algorithm [8].…”

Section: Introductionmentioning

confidence: 99%

“…The Apriori algorithm is the popular and first developed algorithm for frequent pattern mining, but the main limitation is, it requires multiple scanning of transaction datasets and generates huge number of candidate itemsets that reduce the efficiency of algorithm [8]. Eclat algorithm uses the vertical data format and applies the depth-first search technique that is faster than Aprori and requires less memory for small datasets [5]. It requires more time and space when the number of transactions is relatively large.…”

Section: Introductionmentioning

confidence: 99%

Seasonal and Multilevel Association Based Approach for Market Basket Analysis in Retail Supermarket

Rana

Mondal

2021

COMPUTE

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Market Basket Analysis is an observational data mining methodology to investigate the consumer buying behavior patterns in retail Supermarket. It analyzes customer baskets and explores the relationship among products that helps retailers to design store layouts, make various strategic plans and other merchandising decisions that have a big impact on retail marketing and sales. Frequent itemsets mining is the first step for market basket analysis. The association rules mining uncovers the relationship among products by looking what products the customers frequently purchase together. In retail marketing, the transactional database consists of many itemsets that are frequent only in a particular season however not taken into consideration as frequent in general. In some cases, association rules mining at lower data level with uniform support doesn't reflect any significant pattern however there is valuable information hiding behind it. To overcome those problems, we propose a methodology for mining seasonally frequent patterns and association rules with multilevel data environments. Our main contribution is to discover the hidden seasonal itemsets and extract the seasonal associations among products in additionally with the traditional strong regular rules in transactional database that shows the superiority for making season based merchandising decisions. The dataset has been generated from the transaction slips in large supermarket of Bangladesh that discover 442 more seasonal patterns as well as 1032 seasonal association rules in additionally with the regular rules for 0.1% minimum support and 50% minimum confidence.

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An Improved Eclat Algorithm for Mining Association Rules Based on Increased Search Strategy

Abstract: Abstract

Cited by 18 publications

References 17 publications

Multi-Core Processing Cloud Eclat Growth

Multi-Core Processing Cloud Eclat Growth

Data Pattern Of Computer Maintenance Management System With Eclat Algorithm

Seasonal and Multilevel Association Based Approach for Market Basket Analysis in Retail Supermarket

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