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.
A comparative study of efficient initialization methods for the k-means clustering algorithm
2012772 citationsM. Emre Celebi, Hassan A. Kingravi et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by M. Emre Celebi
Since
Specialization
Citations
This map shows the geographic impact of M. Emre Celebi'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. Emre Celebi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Emre Celebi more than expected).
This network shows the impact of papers produced by M. Emre Celebi. 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. Emre Celebi. The network helps show where M. Emre Celebi may publish in the future.
Co-authorship network of co-authors of M. Emre Celebi
This figure shows the co-authorship network connecting the top 25 collaborators of M. Emre Celebi.
A scholar is included among the top collaborators of M. Emre Celebi 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. Emre Celebi. M. Emre Celebi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Celebi, M. Emre, et al.. (2014). Histogram-Based Method for Effective Initialization of the K-Means Clustering Algorithm. The Florida AI Research Society.4 indexed citations
7.
Abbas, Qaisar, et al.. (2013). Features preserving contrast improvement for retinal vascular images. International journal of innovative computing, information & control. 9(9). 3731–3739.2 indexed citations
8.
Celebi, M. Emre, et al.. (2013). An Accelerated Nearest Neighbor Search Method for the K-Means Clustering Algorithm. The Florida AI Research Society.5 indexed citations
9.
Celebi, M. Emre, et al.. (2013). A Simulated Annealing Clustering Algorithm Based On Center Perturbation Using Gaussian Mutation. The Florida AI Research Society.8 indexed citations
10.
Celebi, M. Emre, et al.. (2012). Investigation of Internal Validity Measures for K-Means Clustering. Lecture notes in computer science. 2195(1). 471–476.38 indexed citations
Celebi, M. Emre & Y. Alp Aslandogan. (2005). Human Perception-Driven, Similarity-Based Access to Image Databases. The Florida AI Research Society. 245–250.4 indexed citations
20.
Celebi, M. Emre, et al.. (2005). Skin Lesion Segmentation Using Clustering Techniques. The Florida AI Research Society. 364–369.2 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.