Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Clustering of the self-organizing map
20001.8k citationsJuha Vesanto, Esa AlhoniemiIEEE Transactions on Neural Networksprofile →
SOM-based data visualization methods
1999502 citationsJuha VesantoIntelligent Data Analysisprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Juha Vesanto'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 Juha Vesanto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juha Vesanto more than expected).
This network shows the impact of papers produced by Juha Vesanto. 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 Juha Vesanto. The network helps show where Juha Vesanto may publish in the future.
Co-authorship network of co-authors of Juha Vesanto
This figure shows the co-authorship network connecting the top 25 collaborators of Juha Vesanto.
A scholar is included among the top collaborators of Juha Vesanto based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Juha Vesanto. Juha Vesanto is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Vesanto, Juha, Mika Sulkava, & Jaakko Hollmén. (2003). On the Decomposition of the Self-Organizing Map Distortion Measure. Jukuri (Luonnonvarakeskus Tietopalvelu).25 indexed citations
3.
Vesanto, Juha. (2002). Data exploration process based on the self-organizing map. Aaltodoc (Aalto University).62 indexed citations
Vesanto, Juha & Esa Alhoniemi. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks. 11(3). 586–600.1830 indexed citations breakdown →
6.
Vesanto, Juha, Johan Himberg, & Esa Alhoniemi. (2000). SOM Toolbox for Matlab 5.470 indexed citations
7.
Vesanto, Juha, et al.. (1999). Self-Organizing Map for Data Mining in MATLAB: The SOM Toolbox. 72(25). 824–8.59 indexed citations
8.
Vesanto, Juha, et al.. (1999). Self-organizing map in Matlab: the SOM Toolbox. 35–40.348 indexed citations
Simula, Olli, Juha Vesanto, Esa Alhoniemi, & Jaakko Hollmén. (1999). Analysis and Modeling of Complex Systems Using the Self-Organizing Map.19 indexed citations
11.
Vesanto, Juha & Juha Ahola. (1999). Hunting for Correlations in Data Using the Self-Organizing Map. 279–285.42 indexed citations
Vesanto, Juha. (1999). SOM-based data visualization methods. Intelligent Data Analysis. 3(2). 111–126.502 indexed citations breakdown →
14.
Vesanto, Juha, Johan Himberg, & Esa Alhoniemi. (1999). Publication 6 SelfOrganizing Map in Matlab: the SOM Toolbox.2 indexed citations
15.
Simula, Olli, et al.. (1999). The Self-Organizing Map in Industry Analysis.24 indexed citations
16.
Vesanto, Juha, et al.. (1998). Enhancing SOM Based Data Visualization.16 indexed citations
17.
Simula, Olli, Esa Alhoniemi, Jaakko Hollmén, & Juha Vesanto. (1997). Analysis of Complex Systems Using the Self-Organizing Map. International Conference on Neural Information Processing. 1313–1317.8 indexed citations
18.
Alhoniemi, Esa, Olli Simula, & Juha Vesanto. (1996). Monitoring and Modeling of Complex Processes Using the Self-Organizing Map.3 indexed citations
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.