Edwin Diday

122 papers receiving 3.5k citations

Peers

Edwin Diday
Comparison fields: 5 of 157
  • Statistics and Probability 763
  • Signal Processing 817
  • Artificial Intelligence 2.2k
  • Computational Theory and Mathematics 815
  • Management Science and Operations Research 376
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Francisco de A.T. de Carvalho Brazil
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Countries citing papers authored by Edwin Diday

Since Specialization
Citations

This map shows the geographic impact of Edwin Diday'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 Edwin Diday with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edwin Diday more than expected).

Fields of papers citing papers by Edwin Diday

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Edwin Diday. 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 Edwin Diday. The network helps show where Edwin Diday may publish in the future.

Co-authors

The 25 scholars most cited alongside Edwin Diday, 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 Edwin Diday Line = papers co-authored together Edwin Diday links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 128 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
2000411
2 2000350
3 2003249
4 1991234
5 1994197
6 2006197
7 2000178
8 1992160
9 2002132
10
Une nouvelle méthode en classification automatique et reconnaissance des formes la méthode des nuées dynamiques
1971114
11
Extension de l'analyse en composantes principales à des données de type intervalle
1997101
12 198198
13
Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics)
200777
14 198475
15 199970
16
Orders and overlapping clusters by pyramids
198757
17 200353
18 201151
19 200947
20 197344

About Edwin Diday

Edwin Diday is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Signal Processing and Computer Vision and Pattern Recognition, having authored 128 papers that have together received 3.8k indexed citations. Recurring topics across this work include Rough Sets and Fuzzy Logic (34 papers), Data Mining Algorithms and Applications (27 papers), Advanced Clustering Algorithms Research (23 papers), Data Management and Algorithms (19 papers), Neural Networks and Applications (18 papers), Sensory Analysis and Statistical Methods (10 papers), Bayesian Methods and Mixture Models (9 papers) and Face and Expression Recognition (7 papers). The work is most often cited by research in Statistics and Probability (763 citations), Signal Processing (817 citations), Artificial Intelligence (2.2k citations), Computational Theory and Mathematics (815 citations) and Management Science and Operations Research (376 citations). Edwin Diday has collaborated with scholars based in France, United States and Canada. Frequent co-authors include Lynne Billard, Hans Hermann Bock, Hans‐Hermann Bock, K. Chidananda Gowda, Yves Lechevallier, Patrice Bertrand, Bernard Burtschy, Martin Schader, Richard Emilion and Robert R. Sokal. Their work appears in journals such as Pattern Recognition Letters, Discrete Applied Mathematics, Journal of the American Statistical Association, Advances in Data Analysis and Classification and Statistical Analysis and Data Mining The ASA Data Science Journal.

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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