Javier Muguerza

3.0k citations
80 papers · 1.8k indexed · 1 hit paper · h-index 16

Javier Muguerza

70 papers receiving 1.7k citations

Hit Papers

An extensive comparative study of cluster validity indices8782012202620162021250500750

Peers

Javier Muguerza
Comparison fields: 5 of 169
  • Artificial Intelligence 669
  • Signal Processing 208
  • Statistical and Nonlinear Physics 150
  • Media Technology 91
  • Computer Vision and Pattern Recognition 201
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Jin Liu China
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Peter Eklund Australia
Nan Li China
Russell Beale United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Javier Muguerza

Since Specialization
Citations

This map shows the geographic impact of Javier Muguerza's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Javier Muguerza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javier Muguerza more than expected).

Fields of papers citing papers by Javier Muguerza

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Javier Muguerza. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Javier Muguerza. The network helps show where Javier Muguerza may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Javier Muguerza, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Javier Muguerza Line = papers co-authored together Javier Muguerza links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20240
2 20221
3 202032
4 20194
5 20196
6 20181
7 20171
8 201612
9 201610
10 20151
11
Aprendizaje cooperativo y basado en proyectos en la asignatura Arquitectura de Computadores
20130
12
La herencia filosófica de la Casa de España en México: José Gaos y el pensamiento de lengua española
20130
13 20130
14 20121
15 2012221
16
Una mirada atrás y otra adelante (respuesta a Francisco Vázquez)
20101
17
Unsupervised anomaly detection system for Nidis-s based on payload and probabilistic suffix trees.
20091
18
Evaluation of malware clustering based on its dynamic behaviour
200813
19
Del yo (¿trascendental?) al nosotros (¿intrascendente?): la lectura de Kant en el neokantismo contemporáneo
20041
20 199635

About Javier Muguerza

Javier Muguerza is a scholar working on General Social Sciences, Human Factors and Ergonomics and Computer Science Applications, having authored 80 papers that have together received 1.8k indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (7 papers), Machine Learning and Data Classification (6 papers), Advanced Clustering Algorithms Research (6 papers), Data Mining Algorithms and Applications (5 papers), EEG and Brain-Computer Interfaces (5 papers), Neural Networks and Applications (4 papers), Complex Network Analysis Techniques (4 papers) and Philosophical Thought and Analysis (4 papers). The work is most often cited by research in Artificial Intelligence (669 citations), Signal Processing (208 citations) and Statistical and Nonlinear Physics (150 citations). Javier Muguerza has collaborated with scholars based in Spain, Portugal and Germany. Frequent co-authors include Olatz Arbelaitz, Ibai Gurrutxaga, Jesús M. Pérez, Iñigo Perona, J. Martín, Micael J. T. Oliveira, A. Arruabarrena, Xavier Andrade, Ángel Rubio and Joseba Alberdi‐Rodriguez. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Expert Systems with Applications.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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