Analysis of FP-growth and Apriori algorithms on pattern discovery from weblog data

Dharmarajan, K.; Dorairangaswamy, M. A.

doi:10.1109/icaca.2016.7887945

Cited by 21 publications

(13 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Both algorithms are helpful in analyzing website usage patterns and the characteristics of user behavior. This can be used to enhance web design, introduce personalized services, and promote more effective browsing [13]. By applying the web usage mining method to the clickstream data recorded in the server-side log file, the online consumer behavior on the e-retailer's website can be found.…”

Section: The Association Rules Mining For Errors Detectionsmentioning

confidence: 99%

“…Use FP-Growth algorithm for research, because it has the concept of tree construction when performing itemsets search. A software system for determining educational promotion strategies using this method was developed [13]. A collaborative educational data mining tool based on association rule mining has been developed.…”

Section: The Association Rules Mining For Errors Detectionsmentioning

confidence: 99%

“…The FP-Growth algorithm [9][10][11][12] presents a unique way to identify frequent itemsets without using candidate generations. Some current data mining applications, such as web usage mining and promoting education, are implemented by the FP-Growth algorithm, thus improving performance [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

et al. 2021

View full text Add to dashboard Cite

Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and a (3) customer service strategy.

show abstract

Section: The Association Rules Mining For Errors Detectionsmentioning

confidence: 99%

Section: The Association Rules Mining For Errors Detectionsmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…This process changes or transforms data from the initial data into data following the format of the data mining process [18] without changing the contents of the data [15] [19]. In this stage the process of summarizing (aggregation), pivoting and changing numeric types to the binominal.…”

Section: Transformation Datamentioning

confidence: 99%

A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

Hartono¹,

Fitrianah²

2020

IJCA

View full text Add to dashboard Cite

This study aims to find interesting patterns on the database transaction so that it can be used as a recommendation sales promotion and inventory product. Companies have difficulty finding interesting transaction patterns in large databases, so it will be difficult to determine the right product promotions and inventory. To resolve these problems are to use data mining techniques with association rule. In previous studies, most studies adopt Apriori algorithm to analyze the association rules. In this study, the data mining technique used is the association rules algorithm FP-Growth. In the FP-Growth algorithm did generate candidates as in Apriori algorithm and using a development concept Tree in frequent itemset search so that it requires faster than Apriori. Some of the analyzes produced in this study are higher minimum support values and minimum trust used will result in fewer items and association rules. Association rule in this study has a lift ratio value of more than 1.00, meaning that item K and L are actually bought together. The higher the lift ratio produced shows the stronger the association rules are formed. The results of this study are the minimum confidence of 97.63%, the maximum trust is 99.37% and the lift ratio is 1,00013798. These results can be used as recommendations for optimizing product promotions and inventory.

show abstract

“…To determine the data pattern based on the availability, two practical algorithms exists namely FP-growth and Apriori algorithm to classify user behaviour based on data pattern identification [7]. When compared to FP-growth Apriori algorithm is more effective in terms of network performance and time complexity.…”

Section: Related Workmentioning

confidence: 99%

Boolean Based Mining Algorithm for Pattern Discovery Based on Human Interaction

Ramanayagam¹,

Raja²,

Kannan³

2019

IJITEE

View full text Add to dashboard Cite

Mining is a process that provides useful information on surfing and access pattern information based on capturing the behaviour of the user. Semantic knowledge helps to understand how the users will interact with the system. In this paper, we propose a Boolean based APriori Pattern (APP) algorithm to discover pattern based on human interaction using behavioural analysis. In the process of data mining, we have used a Boolean expression that helps to determine the pattern discovery based on the use of frequent pattern by applying association rules. The behavioural analysis is proposed based on the classification of ideas based on comments concerning positive opinion /contrary opinion during human interaction in the practical scenarios. The behavioural analysis is represented as a tree hierarchy where tree based mining is performed by the tree construction and interaction of flow patterns i.e., frequent patterns. The study shows that the successful pattern can be extracted based on the behavioural analysis of human interaction such as frequent pattern, flow interaction and relationships between the interactions.

show abstract

Analysis of FP-growth and Apriori algorithms on pattern discovery from weblog data

Cited by 21 publications

References 7 publications

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

Boolean Based Mining Algorithm for Pattern Discovery Based on Human Interaction

Contact Info

Product

Resources

About