Comparative Evaluation of Four Methods for Exploratory Data Analysis

Uzhga-Rebrov, Oleg; Grabusts, Peter

doi:10.1109/itms52826.2021.9615347

Cited by 4 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of graph building, the objective of this activity is to identify the entities, properties, and their relationship useful to answer each question defined in the previous stage. For guidance in the exploratory data analysis (EDA), the reader can refer to [42,43], and for a review, a comparative study of four methods employed in EDA, they can refer to [44]. (2.2) Graph Model Design defines which data will be represented as nodes, establishes the relationships between them, and defines the attributes associated with each element.…”

Section: Stage 2: Graph Buildingmentioning

confidence: 99%

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Ortega-Guzmán,

Gutiérrez-Preciado,

Cervantes

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Graph mining has emerged as a significant field of research with applications spanning multiple domains, including marketing, corruption analysis, business, and politics. The exploration of knowledge within graphs has garnered considerable attention due to the exponential growth of graph-modeled data and its potential in applications where data relationships are a crucial component, and potentially being even more important than the data themselves. However, the increasing use of graphs for data storing and modeling presents unique challenges that have prompted advancements in graph mining algorithms, data modeling and storage, query languages for graph databases, and data visualization techniques. Despite there being various methodologies for data analysis, they predominantly focus on structured data and may not be optimally suited for highly connected data. Accordingly, this work introduces a novel methodology specifically tailored for knowledge discovery in labeled and heterogeneous graphs (KDG), and it presents three case studies demonstrating its successful application in addressing various challenges across different application domains.

show abstract

Section: Stage 2: Graph Buildingmentioning

confidence: 99%

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Ortega-Guzmán,

Gutiérrez-Preciado,

Cervantes

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Data scientists [14] examine and analyze data sets and epitomize their key properties using exploratory data analysis (EDA), which is regularly, employs data visualization techniques. It enables the researchers to extract the useful patterns, determine the anomalies, and verify the assumptions by identifying how to change the data assets for improved accuracy.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Stacking [14] [1] classi er is an ensemble learning method that incorporate multiple classi cation models to improve the veracity and robustness of predictions. It works by training several base classi ers on the same dataset, using different algorithms or hyper parameters.…”

Section: Stacking Classi Ermentioning

confidence: 99%

Student Academic Performance prognosticative Using optimized Hybrid Machine Learning Algorithms

Kannan

Abarna

Vairachilai

2023

Preprint

View full text Add to dashboard Cite

Prediction of student performance at early stage in higher education is important for academic society so that strategic decisions can be made before students are placed to keep them from dropping out of the course. Due to India's massive student population and extremely ancient educational system, there are significant difficulties in measuring and forecasting students' performance. Every institution in India has its own unique set of criteria for measuring student achievement, and there is no formal process for keeping track of and evaluating a student's progress and improvement. Over the last decade, researchers in the education domain have presented numerous types of machine learning techniques. However, there are significant obstacles to dealing with imbalanced datasets in order to predict the performance of students. In this paper, the first phase of traditional classification algorithms has been applied to the dataset, which contains the progress of 4424 students. In the second phase, novel hybrid machine learning (ML) algorithms were used to get better predictions. The outcome of the proposed model makes it easier to predict how well students will do so that early decisions can be made about the growth of higher education institutions.

show abstract

High Performance EDA and LDA Analysis: An Application for Wheat Yield Estimation

Kumar,

Kukreja

et al. 2023

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)

View full text Add to dashboard Cite

Comparative Evaluation of Four Methods for Exploratory Data Analysis

Cited by 4 publications

References 11 publications

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Student Academic Performance prognosticative Using optimized Hybrid Machine Learning Algorithms

High Performance EDA and LDA Analysis: An Application for Wheat Yield Estimation

Contact Info

Product

Resources

About