Association Rules Mining in Big Data

Shakhovska, Nataliya; Kaminskyy, Roman; Zasoba, Eugen; Tsiutsiura, Mykola

doi:10.47839/ijc.17.1.946

Cited by 24 publications

(5 citation statements)

References 14 publications

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“…Considering the significant practical and theoretical results of research in related fields, web projects should be analyzed as heterogeneous data environments [1][2][3][4][5][6] and as content sources [7][8][9][10]. As conventional messaging and news-distribution-oriented web projects are gradually being transformed into video hosting with the ability to stream video online in real time [11][12][13][14], the speed of information is measured in seconds.…”

Section: Introductionmentioning

confidence: 99%

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

et al. 2020

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This article discusses the relevant task of analyzing user data in the process of managing various web projects. The results of this analysis will help to improve the management of diverse web projects during crises. The authors explore the concept of data heterogeneity in web projects, classify web projects by function and purpose, and analyze the search models and data display in web projects. The proposed algorithms for analyzing user data in the process of managing diverse web projects will improve the structuring and presentation of data on the web project platform. The model user data analysis complex developed by the authors will simplify the process of managing various web projects during crises.

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Section: Introductionmentioning

confidence: 99%

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

et al. 2020

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“…Expected values are calculated for associations that have at least two features on the left side of the rule, assuming that they are statistically independent. Informativeness makes it possible to assess "curiosity" if, for example, the level of support for the rule is R1 times higher than the expected value, and the level of trust is R2 times lower and vice versa [10]. The visualization of hidden dependencies is given in Figure 9.…”

Section: Conf(xmentioning

confidence: 99%

Auralization of Concert Halls for Touristic Purposes

Schauer

Bertocci

Cioli

et al. 2022

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This paper presents the progress implemented during the AURA project, funded by the Creative-Europe program with project partners from Germany, Italy, and Ukraine. The project aims to create auralized applications for three music venues in each of the project countries, namely the Konzerthaus Berlin, the Teatro del Maggio in Florence, and the Opera House Lviv. Each will be digitally recreated and auralized before they are then used to conduct case studies. This paper gives insights into current digitalization and auralization techniques. The results of a digital survey will be laid out and the conception and implementation of a first auralized prototype using a hand-modeled 3D object from the Great Hall of the Konzerthaus Berlin will be demonstrated. Furthermore, the usage of auralization for touristic purposes will be investigated using artificial intelligence for an audience preference analysis. A conclusion will be drawn and a short outlook into the ongoing course of the AURA project will be given.

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“…Then, relationships between patient parameters, indicators, and contraindicators of medicaments from the recommended list should be found. To do this, the a priori algorithm [31] can be used.…”

Section: The Algorithm Used For Finding Personalized Treatmentmentioning

confidence: 99%

Data-Driven Analytics for Personalized Medical Decision Making

et al. 2020

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The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent and time-independent parameters. Personalized treatment is usually based on the decision tree method. This approach requires significant computation time and cannot be parallelized. Therefore, it was proposed to group people by conditions and to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. The novelty of the paper is the new clustering method, which was built from an ensemble of cluster algorithms, and the usage of the new distance measure with Hopkins metrics, which were 0.13 less than for the k-means method. The Dunn index was 0.03 higher than for the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm. The next stage was the mining of associative rules provided separately for each cluster. This allows a personalized approach to treatment to be created for each patient based on long-term monitoring. The correctness level of the proposed medical decisions is 86%, which was approved by experts.

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Association Rules Mining in Big Data

Cited by 24 publications

References 14 publications

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

Auralization of Concert Halls for Touristic Purposes

Data-Driven Analytics for Personalized Medical Decision Making

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