Mining Web Access Sequence with Improved Apriori Algorithm

Yang, Jun; Huang, Hongyun; Jin, Xiaohui

doi:10.1109/cse-euc.2017.154

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…The Apriori algorithm was modified by counting only the sequential transactions that are frequent so that transaction (a, b) is not the same as transaction (b, a). Furthermore, in sequence data mining, many Apriorilike or Apriori-based algorithms have been proposed by researchers, such as mining Web access sequence [15], or modifying the association rules by adding time constraint [16]. Meanwhile, in the melodic pattern, Apriori-based algorithms have been developed by [17] to generate music, and the Apriori based on Function in a Sequence (AFiS) algorithm proposed by [18] counted only the sequential transactions that are frequent with additional procedures in the form of measuring musical elements based on their position in the sequence as functions to identify frequent sequence patterns.…”

Section: Related Workmentioning

confidence: 99%

Melody Difficulty Classification using Frequent Pattern and Inter-Notes Distance Analysis

Andono¹,

Noersasongko²,

Shidik³

et al. 2022

IJACSA

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This research proposes a novel method for melody difficulty classification performed using frequent pattern and inter-notes distance analysis. The Apriori algorithm was used to measure the frequency of the notes in the note sequence, in which the melody length is also included in the calculation. In addition, the inter-notes distance analysis was also used to measure the difficulty level of composition based on the distance between successive notes. The classification was performed for traditional Javanese compositions known as Gamelan music. Symbolic representation, in which the Gamelan compositions music sheets were collected as the dataset, was chosen by asking experts to divide the compositions based on their difficulty level into basic, intermediate and advanced classes. Then, the proposed method was implemented to measure the difficulty value of each composition. The difference in the interpretation of the difficulty level between the experts and the difficulty value of the composition is solved by calculating the mean value to obtain the range of difficulty values in each class. Evaluation was performed using confusion matrix to measure the accuracy, precision and recall value, and the results reaching 82%, 82.1% and 82%, respectively.

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Section: Related Workmentioning

confidence: 99%

Melody Difficulty Classification using Frequent Pattern and Inter-Notes Distance Analysis

Andono¹,

Noersasongko²,

Shidik³

et al. 2022

IJACSA

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“…Developed by Agrawal (Jeeva & Rajsingh, 2016) and Sriknat (Raj et al, 2020). Although the performance of the classical Apriori algorithm using the iterative method (Yang et al, 2017) cannot compete with sophisticated depth-first approaches. Therefore the basic idea of finding all frequent items in a given database is universal and easy to apply to any rule asosiasi (Hong et al, 2020) mining problem though approach depth-first (Yuan, 2017).…”

Section: Apriori Algorithmmentioning

confidence: 99%

“…This Market Basket Analysis is considered capable of providing a way out in recommending a combination of product categories related to the use of the Apriori algorithm through the Association Rules method. The Apriori Algorithm was invented by Agrawal (Wang and Zheng 2020) and Srikant (Raj et al, 2020) 1994(Yang et al, 2017.…”

Section: Introductionmentioning

confidence: 99%

Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Santoso

2021

Brilliance

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Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

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“…Web Access Sequences (WAS) (Yang et al, 2017;Ting et al, 2009) are ordered sequences of pages accessed in a session. They can be extracted either from log files or recorded from low-level events, in an online or offline manner.…”

Section: Sequencementioning

confidence: 99%

A review of alignment based similarity measures for web usage mining

et al. 2019

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In order to understand web-based application user behavior, web usage mining applies unsupervised learning techniques to discover hidden patterns from web data that captures user browsing on web sites. For this purpose, web session clustering has been among the most popular approaches to group users with similar browsing patterns that reflect their common interest. An adequate web session clustering implementation significantly depends on the measure that is used to evaluate the similarity of sessions. An efficient approach to evaluate session similarity is sequence alignment, which is known as the task of determining the similarity of elements between sequences. In this paper, we review and compare sequence alignment-based measures for web sessions, and also discuss sequence similarity measures that are not alignment-based. This review also provides a perspective of sequence similarity measures that manipulate web sessions in usage clustering process.

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Mining Web Access Sequence with Improved Apriori Algorithm

Cited by 15 publications

References 10 publications

Melody Difficulty Classification using Frequent Pattern and Inter-Notes Distance Analysis

Melody Difficulty Classification using Frequent Pattern and Inter-Notes Distance Analysis

Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

A review of alignment based similarity measures for web usage mining

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