Byron Dom

10.6k citations
52 papers · 5.7k indexed · 2 hit papers · h-index 24

Byron Dom

47 papers receiving 5.0k citations

Hit Papers

Focused crawling: a new approach to topic-specific Web re...936199520262005201550010001.5k

Peers

Byron Dom
Comparison fields: 5 of 120
  • Computer Vision and Pattern Recognition 2.4k
  • Information Systems 2.5k
  • Signal Processing 859
  • Artificial Intelligence 2.0k
  • Statistical and Nonlinear Physics 699
Replace Kamal Nigam with:
Kamal Nigam United States
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Byron Dom relative to Kamal Nigam United States Kamal Nigam's profile →
Citations per field
00.5×1.6×
Kamal Nigam · 1×
Citations per year

Countries citing papers authored by Byron Dom

Since Specialization
Citations

This map shows the geographic impact of Byron Dom'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 Byron Dom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Byron Dom more than expected).

Fields of papers citing papers by Byron Dom

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Byron Dom. 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 Byron Dom. The network helps show where Byron Dom may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Byron Dom, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Byron Dom Line = papers co-authored together Byron Dom links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20138
2 200813
3 200672
4 20051
5 200311
6
An information-theoretic external cluster-validity measure
200293
7 2002108
8 20016
9
Who is SMILing on the Web
20012
10
Hierarchical Unsupervised Learning
20006
11
Distributed Hypertext Resource Discovery Through Examples
199942
12
Generalized Model Selection for Unsupervised Learning in High Dimensions
199938
13
Model Selection in Unsupervised Learning with Applications To Document Clustering
199936
14
Mining Surprising Patterns Using Temporal Description Length
199877
15 199882
16
Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases
199785
17 199534
18 19940
19
Machine Vision Applications in Character Recognition and Industrial Inspection
19923
20 19921

About Byron Dom

Byron Dom is a scholar working on Information Systems, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Artificial Intelligence and Computer Science Applications, having authored 52 papers that have together received 5.7k indexed citations. Recurring topics across this work include Web Data Mining and Analysis (14 papers), Text and Document Classification Technologies (11 papers), Industrial Vision Systems and Defect Detection (9 papers), Image and Object Detection Techniques (7 papers), Medical Image Segmentation Techniques (6 papers), Algorithms and Data Compression (6 papers), Expert finding and Q&A systems (5 papers) and Complex Network Analysis Techniques (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.4k citations), Information Systems (2.5k citations), Signal Processing (859 citations), Artificial Intelligence (2.0k citations) and Statistical and Nonlinear Physics (699 citations). Byron Dom has collaborated with scholars based in United States, India and South Korea. Frequent co-authors include Soumen Chakrabarti, Martin van den Berg, Piotr Indyk, W. Niblack, Prabhakar Raghavan, Dragutin Petković, Myron Flickner, Qian Huang, David A. Steele and J. Ashley. Their work appears in journals such as Machine Vision and Applications, Computer, Artificial Intelligence Review, ACM SIGMOD Record and The VLDB Journal.

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.

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