Mining clique frequent approximate subgraphs from multi-graph collections

Acosta-Mendoza, Niusvel; Carrasco-Ochoa, Jesús Ariel; Martínez-Trinidad, José Fco.; Gago-Alonso, Andrés; Medina-Pagola, José E.

doi:10.1007/s10489-019-01564-8

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…If it is not less than the minimum support count, save the column vector to yðW j Þ. The item set corresponding to the finally retained column vector is the frequent item [18,19] 2.2. Data Cleaning.…”

Section: Datamentioning

confidence: 99%

Application of Fuzzy $K$ -Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method

2022

Wireless Communications and Mobile Computing

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As an important course in colleges, the teaching quality of English courses directly affects the efficiency of talent training. Carrying out English teaching evaluation is conducive to solve the problems existing in English teaching timely, improve the teaching level, and promote the demand of English curriculum teaching reform. However, the traditional English teaching evaluation methods adopted by colleges have some disadvantages, such as inaccurate evaluation results and long evaluation time, which urgently needs to innovate and reform the English teaching evaluation method. With the wide application of modern information technology in English teaching, the fuzzy K -means clustering algorithm can be used to construct a new English teaching evaluation model, which can effectively make up for the shortcomings of traditional teaching evaluation methods. This study proposes an English teaching evaluation method based on the fuzzy K -means clustering algorithm. This study uses the association rule analysis method under the data mining technology to preprocess the English teaching data, establishes the hierarchical structure model combined with the applying steps of the analytic hierarchy, and constructs the judgment matrix, carries out the hierarchical ranking and consistency test, and defines the specific weight of each index in the teaching evaluation index system, transforms the problem of English teaching evaluation into solving the K-means clustering objective function, realizes the scientific and accurate evaluation of English teaching, and provides a guarantee for improving the quality of English teaching. The results show that the English teaching evaluation method based on the fuzzy K -means clustering algorithm has a good evaluation accuracy, ensuring that the evaluation error is less than 6%, and the evaluation time is only 3.9 seconds, which significantly reduces the evaluation time.

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Section: Datamentioning

confidence: 99%

Application of Fuzzy $K$ -Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method

2022

Wireless Communications and Mobile Computing

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“…• It would be valuable to extract rules from the high-level visualizations that we obtained. For example, Acosta-Mendoza et al propose a frequent approximate subgraph mining approach [51], which we could incorporate as the last step of our methodology.…”

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A High-Level Representation of the Navigation Behavior of Website Visitors

Huidobro

Monroy

Cervantes³

2022

Applied Sciences

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Knowing how visitors navigate a website can lead to different applications. For example, providing a personalized navigation experience or identifying website failures. In this paper, we present a method for representing the navigation behavior of an entire class of website visitors in a moderately small graph, aiming to ease the task of web analysis, especially in marketing areas. Current solutions are mainly oriented to a detailed page-by-page analysis. Thus, obtaining a high-level abstraction of an entire class of visitors may involve the analysis of large amounts of data and become an overwhelming task. Our approach extracts the navigation behavior that is common among a certain class of visitors to create a graph that summarizes class navigation behavior and enables a contrast of classes. The method works by representing website sessions as the sequence of visited pages. Sub-sequences of visited pages of common occurrence are identified as “rules”. Then, we replace those rules with a symbol that is given a representative name and use it to obtain a shrinked representation of a session. Finally, this shrinked representation is used to create a graph of the navigation behavior of a visitor class (group of visitors relevant to the desired analysis). Our results show that a few rules are enough to capture a visitor class. Since each class is associated with a conversion, a marketing expert can easily find out what makes classes different.

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Intelligent Mining Method of New Media Art Image Features Based on Multi-scale Rule Set

Xu,

Sun

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Mining clique frequent approximate subgraphs from multi-graph collections

Cited by 3 publications

References 27 publications

Application of Fuzzy $K$ -Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method

Application of Fuzzy $K$ -Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method

A High-Level Representation of the Navigation Behavior of Website Visitors

Intelligent Mining Method of New Media Art Image Features Based on Multi-scale Rule Set

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Mining clique frequent approximate subgraphs from multi-graph collections

Cited by 3 publications

References 27 publications

Application of Fuzzy K -Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method

Application of Fuzzy K -Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method

A High-Level Representation of the Navigation Behavior of Website Visitors

Intelligent Mining Method of New Media Art Image Features Based on Multi-scale Rule Set

Contact Info

Product

Resources

About

Application of Fuzzy $K$ -Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method

Application of Fuzzy $K$ -Means Clustering Algorithm in the Innovation of English Teaching Evaluation Method