Pattern-Based and Visual Analytics for Visitor Analysis on Websites

Cervantes, Bárbara; Gómez, Fernando Aguilar; Monroy, Raúl; Loyola‐González, Octavio; Medina‐Pérez, Miguel Angel; Ramírez-Márquez, José Emmanuel

doi:10.3390/app9183840

Cited by 12 publications

(10 citation statements)

References 23 publications

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“…There are other clickstream pattern mining approaches, but they are focused on improving the runtime or memory consumption for clustering visitors [21,22]. Visualization tools have also been proposed to analyze the navigation behavior of visitors [34][35][36][37][38][39]. However, they provide a detailed analysis of web pages; for example, to find the percentage of visitors on each web page.…”

Section: Other Non-commercial Approachesmentioning

confidence: 99%

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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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Section: Other Non-commercial Approachesmentioning

confidence: 99%

“…B. Cervantes [35] They combine visualization and machine learning techniques for analyzing web log data. The visualization can be used by business experts to obtain insights by looking at key elements of the graph.…”

Section: Nonmentioning

confidence: 99%

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

Huidobro

Monroy

Cervantes³

2022

Applied Sciences

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“…The main idea is to provide both accurate and explainable models for experts in the application area. Pattern-based Machine Learning has proven to be an approach showing an accurate and explainable model in several practical applications, such as visitor analysis on websites [42], understanding the criminal behavior [43], bot detection [44], and detecting pneumatic failures on temporary immersion bioreactors [45]; among others. DT.Children = BuildDT(DS);…”

Section: B Numerical Patternsmentioning

confidence: 99%

An Explainable Artificial Intelligence Model for Clustering Numerical Databases

et al. 2020

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Nowadays, the international scientific community of machine learning has an enormous campaign in favor of creating understandable models instead of black-box models. The main reason is that experts in the application area are showing reluctance due to black-box models cannot be understood by them, and consequently, their results are difficult to be explained. In unsupervised problems, where experts have not labeled objects, obtaining an explanation of the results is necessary because specialists in the application area need to understand both the applied model as well as the obtained results for finding the rationale behind each obtained clustering from a practical point of view. Hence, in this paper, we introduce a clustering based on decision trees (eUD3.5), which builds several decision trees from numerical databases. Unlike previous solutions, our proposal takes into account both separation and compactness for evaluating a feature split without decreasing time efficiency and with no empirical parameter to control the depth of the trees. We tested eUD3.5 on 40 numerical databases of UCI Machine Learning Repository, showing that our proposal builds a set of high-quality unsupervised decision trees for clustering, allowing us to obtain the best average ranking compared with other popular state-of-the-art clustering solutions. Also, from the collection of unsupervised decision trees induced by our proposal, a set of high-quality patterns are extracted for showing the main feature-value pairs describing each cluster. INDEX TERMS Explainable model, clustering, unsupervised decision trees, numerical databases.

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“…21 To complement the mathematical representation of the contrast patterns, we visualize the data according to the patterns for better comprehension. Combining machine learning results with data visualization is becoming a popular technique currently [46]- [50] because it allows the user to see the information from different points of view.…”

Section: B Analyzing the Extracted Patternsmentioning

confidence: 99%

A Contrast Pattern-Based Scientometric Study of the QS World University Ranking

et al. 2020

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Despite the shortcomings and criticisms of world university rankings, such metrics are widely used by students and parents to select institutions and by educational institutions to attract talented students and researchers, as well as funding. This article introduces the first contrast pattern-based scientometric study of world university rankings. Specifically, this study collects a database containing 34 features, which describe the essential research indicators for the top 200 universities in the Quacquarelli Symonds (QS) ranking. The use of 18 state-of-the-art classifiers in this database shows that the top 100 universities in the QS World University Rankings are separable from the remaining compared universities, achieving an average accuracy of 71%. Additionally, using a contrast pattern mining algorithm, a set of patterns describing the top 100 universities is extracted based on scientometric features. Additionally, this study proposes an approach for visualizing the extracted patterns to facilitate the decision-makers, such as senior university managers, in formulating and evaluating their research (ranking) strategies.INDEX TERMS University rankings, data mining, scientometrics, contrast patterns.

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Pattern-Based and Visual Analytics for Visitor Analysis on Websites

Cited by 12 publications

References 23 publications

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

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

An Explainable Artificial Intelligence Model for Clustering Numerical Databases

A Contrast Pattern-Based Scientometric Study of the QS World University Ranking

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