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.
Algorithm AS 136: A K-Means Clustering Algorithm
19799.9k citationsJ. A. Hartigan, M. Anthony WongJournal of the Royal Statistical Society Series C (Applied Statistics)profile →
Countries citing papers authored by M. Anthony Wong
Since
Specialization
Citations
This map shows the geographic impact of M. Anthony Wong'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 M. Anthony Wong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Anthony Wong more than expected).
This network shows the impact of papers produced by M. Anthony Wong. 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 M. Anthony Wong. The network helps show where M. Anthony Wong may publish in the future.
Co-authorship network of co-authors of M. Anthony Wong
This figure shows the co-authorship network connecting the top 25 collaborators of M. Anthony Wong.
A scholar is included among the top collaborators of M. Anthony Wong 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 M. Anthony Wong. M. Anthony Wong is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
17 of 17 papers shown
#
Work
Indexed citations
1
Asymptotic Properties of Univariate Population K-Means Clusters
2018·DSpace@MIT (Massachusetts Institute of Technology)·M. Anthony Wong
1
2
Asymptotic Properties of K-Means Clustering Algorithm as a Density Estimation Procedure
2018·DSpace@MIT (Massachusetts Institute of Technology)·M. Anthony Wong
1
3
An empirical study of the chi-square probability plot for assessing multivariate normality
2011·DSpace@MIT (Massachusetts Institute of Technology)·M. Anthony Wong,
(unknown)
1
4
Using the Kth nearest neighbor clustering procedure to determine the number of subpopulations
2011·DSpace@MIT (Massachusetts Institute of Technology)·M. Anthony Wong,
(unknown)
3
5
A Hybrid Clustering Algorithm for Identifying High Density Clusters
2011·DSpace@MIT (Massachusetts Institute of Technology)·M. Anthony Wong
3
6
Using the K-means clustering method as a density estimation procedure
2011·DSpace@MIT (Massachusetts Institute of Technology)·M. Anthony Wong
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.