Mohammed J. Zaki: h-index, Total Citations, and Citation Map
Mohammed J. Zaki's h-index is 84 (212 i10-index, 37,880+ total citations across 3+ publications) according to Google Scholar as of July 2026. Mohammed J. Zaki is affiliated with Professor & Head, Computer Science Department, RPI.
Mohammed J. Zaki is a researcher affiliated with Professor & Head, Computer Science Department, RPI, specializing in Data Mining, Machine Learning, Graph Mining. Their work has been cited 37,880 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Mohammed J. Zaki's Citation Metrics
Bibliometric impact based on 3 indexed publications.
- H-Index
- 84
- i10-Index
- 212
- Total Citations
- 37,880
- Citing Countries
- 18
As of July 2026.
Mohammed J. Zaki has an h-index of 84 and 37,880 total citations across 3 publications, with research cited by institutions in 18 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Mohammed J. Zaki's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Scalable Algorithms for Association Mining
20022,578
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher developed foundational algorithms for the fast and scalable discovery of association rules, establishing a critical methodological framework for efficient data mining.
The researcher developed CHARM, an efficient algorithm for closed itemset mining, establishing a foundational method for compactly representing frequent patterns in data mining.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
About Mohammed J. Zaki's research
Mohammed J. Zaki is a researcher in Data Mining, Machine Learning and Graph Mining at Professor & Head, Computer Science Department, RPI. Their work has been cited 37,880 times across 3 publications (h-index 84), according to Google Scholar.
Their most-cited work, “Scalable Algorithms for Association Mining” (2002), has accumulated 2,578 citations. Other influential works include “New Algorithms for Fast Discovery of Association Rules” (1997) with 1,947 citations and “CHARM: An Efficient Algorithm for Closed Itemset Mining” (2002) with 1,946 citations.
Citations of Mohammed J. Zaki's research come primarily from United States, Australia and China, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
