A Study on Milestones of Association Rule Mining Algorithms in Large Databases

Suba, Saravanan; T, Chistopher. T. Chistopher.

doi:10.5120/7167-9674

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…There are typically two ways to get over these restrictions: a) One is to experiment with various pruning and filtering procedures to reduce the size of candidate Item set. b) The second strategy either replaces the original database with a subset of transactions based on a significant number of frequently occurring Item sets or reduces the frequency of database scans [17,29].…”

Section: This Algorithm Has Two Drawbacksmentioning

confidence: 99%

Comparative Analysis of Algorithms for Mining Frequent Patterns

Priya¹

2023

IJRASET

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In the computerized world, everything is moving online, and data comes in different shapes and sizes and is collected in different ways. By using data mining, frequent pattern in the databases can be identified, and it can be used in numerous applications. Finding frequent patterns in huge databases is important because it reveals important information that cannot be found through simple data surfing. To find common patterns, a variety of methods are utilized, each of which performs differently. Apriori and FP Growth are the fundamental algorithms employed in frequent pattern mining. The functioning and experimental results of various algorithms are compared in this study, and their benefits and drawbacks are discussed.

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Section: This Algorithm Has Two Drawbacksmentioning

confidence: 99%

Comparative Analysis of Algorithms for Mining Frequent Patterns

Priya¹

2023

IJRASET

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“…Those interesting knowledge can be significant, implicit, novel, or potentially useful (Han et al, 2011). Association rule mining (ARM; Agrawal, Imieliński, & Swami, 1993; J. Han, 2006; Suresh & Harshni, 2017; Suba & Christopher, 2012) is a sub‐area within the data mining, which aims at discovering interesting frequent patterns, correlation, or association in a dataset. Unfortunately, extracting classical association rules (ARs) from huge data amounts suffers from a diversity of problems such as extraction of repetitive rules, the huge amount of extracted rules, and possible loss of important rules (Ayouni, Yahia, & Laurent, 2011; Helm, 2007; Khiat, Belbachir, & Rahal, 2014).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review on updating concept lattices and its application in updating association rules

Shemis

Mohammed

2021

WIREs Data Min & Knowl

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Formal concept analysis (FCA) visualizes formal concepts in terms of a concept lattice. Usually, it is an NP‐problem and consumes plenty of time and storage space to update the changes of the lattice. Thus, introducing an efficient way to update and maintain such lattices is a significant area of interest within the field of FCA and its applications. One of those vital FCA applications is the association rule mining (ARM), which aims at generating a loss‐less nonredundant compact Association Rule‐basis (AR‐basis). Currently, the real‐world data rapidly overgrow that asks the need for updating the existing concept lattice and AR‐basis upon data change continually. Intuitively, updating and maintaining an existing concept‐lattice or AR‐basis is much more efficient and consistent than reconstructing them from scratch, particularly in the case of massive data. So far, the area of updating both concept lattice and AR‐basis has not received much attention. Besides, few noncomprehensive studies have focused only on updating the concept lattice. From this point, this article comprehensively introduces basic knowledge regarding updating both concept lattices and AR‐basis with new illustrations, formalization, and examples. Also, the article reviews and compares recent remarkable works and explores the emerging future research trends. This article is categorized under: Algorithmic Development > Association Rules Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Association Rules

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An improved and efficient frequent pattern mining approach to discover frequent patterns among important attributes in large data set using IA-TJ-FGTT

Suba

Christopher²

2016

2016 IEEE International Conference on Advances in Computer Applications (ICACA)

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A Study on Milestones of Association Rule Mining Algorithms in Large Databases

Cited by 6 publications

References 39 publications

Comparative Analysis of Algorithms for Mining Frequent Patterns

Comparative Analysis of Algorithms for Mining Frequent Patterns

A comprehensive review on updating concept lattices and its application in updating association rules

An improved and efficient frequent pattern mining approach to discover frequent patterns among important attributes in large data set using IA-TJ-FGTT

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