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.
Top 10 algorithms in data mining
20073.9k citationsVipin Kumar, Michael Steinbach et al.profile →
Countries citing papers authored by Michael Steinbach
Since
Specialization
Citations
This map shows the geographic impact of Michael Steinbach'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 Michael Steinbach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Steinbach more than expected).
Fields of papers citing papers by Michael Steinbach
This network shows the impact of papers produced by Michael Steinbach. 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 Michael Steinbach. The network helps show where Michael Steinbach may publish in the future.
Co-authorship network of co-authors of Michael Steinbach
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Steinbach.
A scholar is included among the top collaborators of Michael Steinbach 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 Michael Steinbach. Michael Steinbach is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Karpatne, Anuj, Gowtham Atluri, James H. Faghmous, et al.. (2016). Theory-guided Data Science: A New Paradigm for Scientific Discovery.. arXiv (Cornell University).10 indexed citations
Dey, Sanjoy Kumer, et al.. (2015). Predicting the Factors of Improvement of Health Status of Home Health Care Patients: A Holistic Data Mining Approach.. AMIA.1 indexed citations
11.
Pruinelli, Lisiane, Pranjul Yadav, Sanjoy Kumer Dey, et al.. (2015). Clustering Health Data to Discover EBP Interventions for Sepsis Prevention and Treatment for Health Disparities.. AMIA.1 indexed citations
Pruinelli, Lisiane, Sanjoy Kumer Dey, György Simon, et al.. (2014). Data Mining Methodologies to Discover Best practices for Diabetic Patients with Health Disparities.. AMIA.1 indexed citations
14.
Dey, Sanjoy Kumer, et al.. (2013). Data Mining to Predict Mobility Outcomes for Older Adults Receiving Home Health Care.. AMIA.1 indexed citations
15.
Pandey, Gaurav, et al.. (2012). Computational Approaches to Protein Function Prediction. Wiley-Interscience eBooks.7 indexed citations
Joshi, Mahesh V., et al.. (2003). High performance data mining. Lecture notes in computer science. 2565. 111–125.13 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.