Self-optimization Rule-chain Mining Based on Potential Association Rule Directed Graph

Ning, Hongyun; Liu, Jinlan; Zhang, Degan

doi:10.1109/iscid.2008.17

Cited by 2 publications

(11 citation statements)

References 5 publications

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“…First, we evaluated algorithms that were originally compared with Apriori. The results illustrate that NICGAR (Martín et al, 2016), MONPNAR (Martín et al, 2014), G3PARM (Luna et al, 2012), MDS-H (Hong & Bian, 2008), Ant-ARM (He & Hui, 2009), and SRmining (Hong-yun et al, 2008) are the fastest heuristic ARM algorithms compared to Apriori. At that point, we compared approaches that compared themselves with other heuristic approaches.…”

Section: Applications Of Heuristic Algorithmsmentioning

confidence: 96%

“…ACO was the main solution for another approach by Hong-yun, Jin-lan, and De-gan (2008), named SRMining for finding longer rule-chains. First, it creates a graph, named PAGraph, which contains directions to most potential ARs.…”

Section: Approaches Based On Acomentioning

confidence: 99%

“…After all, according to these comparisons, NICGAR (Martín et al, 2016), MONPNAR (Martín et al, 2014), G3PARM (Luna et al, 2012), MDS-H (Hong & Bian, 2008), SRmining (Hong-yun et al, 2008), PMES (Djenouri et al, 2014), Ant-ARM (He & Hui, 2009), and Kuoa et al (2011) have the potential to be the fastest heuristic ARM algorithms. However, there is a big gap in this area and all the heuristic approaches should compare themselves with each other and instead of comparing themselves with Apriori, they should try to establish comparisons with more up-to-date and faster ARM algorithms.…”

Section: Execution Timementioning

confidence: 99%

“…Researchers in Kuo and Shih (2007), Goyal et al (2015), Hong-yun et al (2008), and Narvekar and Syed (2015) considered this parameter in their experimental process. Since Apriori scans the database many times and this number could vary in different databases, we do not include it in Figure 5.…”

Section: Additional Featuresmentioning

confidence: 99%

“…Until now, many ARM algorithms have been proposed to decrease this number even more. ACS (Kuo & Shih, 2007), SRMining (Hong-yun et al, 2008), and Narvekar (Narvekar & Syed, 2015) are such algorithms. They all require to scan the database just once.…”

Section: Additional Featuresmentioning

confidence: 99%

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A survey on association rules mining using heuristics

Ghafari

Tjortjis

2019

WIREs Data Min & Knowl

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Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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