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.
Chameleon: hierarchical clustering using dynamic modeling
19991.4k citationsGeorge Karypis, Eui-Hong Han et al.profile →
Citations per year, relative to Eui-Hong Han Eui-Hong Han (= 1×)
peers
H.-P. Kriegel
Countries citing papers authored by Eui-Hong Han
Since
Specialization
Citations
This map shows the geographic impact of Eui-Hong Han'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 Eui-Hong Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eui-Hong Han more than expected).
This network shows the impact of papers produced by Eui-Hong Han. 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 Eui-Hong Han. The network helps show where Eui-Hong Han may publish in the future.
Co-authorship network of co-authors of Eui-Hong Han
This figure shows the co-authorship network connecting the top 25 collaborators of Eui-Hong Han.
A scholar is included among the top collaborators of Eui-Hong Han 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 Eui-Hong Han. Eui-Hong Han is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bremer, E., Jörg Hakenberg, Eui-Hong Han, Daniel Berrar, & Werner Dubitzky. (2006). Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature.1 indexed citations
5.
Bremer, E., Jörg Hakenberg, Eui-Hong Han, Daniel Berrar, & Werner Dubitzky. (2006). Knowledge Discovery in Life Science Literature: International Workshop, KDLL 2006, Singapore, April 9, 2006, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Bioinformatics).1 indexed citations
Kumar, Sachin, et al.. (2001). Personalized Profile Based Search Interface With Ranked and Clustered Display. University of Minnesota Digital Conservancy (University of Minnesota).
9.
Joshi, Mahesh V., Eui-Hong Han, George Karypis, & Vipin Kumar. (2001). Parallel Algorithms in Data Mining. University of Minnesota Digital Conservancy (University of Minnesota).4 indexed citations
Karypis, George, Eui-Hong Han, & Vipin Kumar. (1999). Multilevel Refinement for Hierarchical Clustering. University of Minnesota Digital Conservancy (University of Minnesota).19 indexed citations
14.
Kumar, Vipin, George Karypis, & Eui-Hong Han. (1999). Text categorization using weight adjusted k-nearest neighbor classification (information retrieval).8 indexed citations
Han, Eui-Hong, George Karypis, Vipin Kumar, & Bamshad Mobasher. (1998). Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results.. IEEE Data(base) Engineering Bulletin. 21. 15–22.77 indexed citations
17.
Han, Eui-Hong, Daniel Boley, Maria Gini, et al.. (1998). WebACE. 408–415.126 indexed citations
Han, Eui-Hong, George Karypis, & Vipin Kumar. (1997). Min-Apriori: An Algorithm for Finding Association Rules in Data with Continuous Attributes. University of Minnesota Digital Conservancy (University of Minnesota).8 indexed citations
20.
Han, Eui-Hong, George Karypis, Vipin Kumar, & Bamshad Mobasher. (1997). Clustering in a High-Dimensional Space Using Hypergraph Models. University of Minnesota Digital Conservancy (University of Minnesota).31 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.