King-Ip Lin

3.3k citations
31 papers · 2.2k indexed · 1 hit paper · h-index 17

King-Ip Lin

29 papers receiving 1.9k citations

Hit Papers

FastMap6181995202620052015200400600

Peers

King-Ip Lin
Comparison fields: 5 of 101
  • Signal Processing 1.3k
  • Computer Vision and Pattern Recognition 805
  • Artificial Intelligence 813
  • Information Systems 517
  • Computer Graphics and Computer-Aided Design 65
Replace Ira Assent with:
Ira Assent Denmark
Stefan Berchtold Germany
Stephen Kelley United States
Paolo Ciaccia Italy
Pavel Zezula Czechia
Marco Patella Italy
Wilfred Ng Hong Kong
Chin‐Wan Chung South Korea
Flip Korn United States
Caetano Traina Brazil
King-Ip Lin relative to Ira Assent Denmark Ira Assent's profile →
Citations per field
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Ira Assent · 1×
Citations per year

Countries citing papers authored by King-Ip Lin

Since Specialization
Citations

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

Fields of papers citing papers by King-Ip Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by King-Ip Lin. 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 King-Ip Lin. The network helps show where King-Ip Lin may publish in the future.

Co-authorship network

The 25 scholars most cited alongside King-Ip Lin, 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 King-Ip Lin Line = papers co-authored together King-Ip Lin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20220
2
Improving Topic Model Visualization via Multi-Dimensional Scaling and Cliques.
20181
3 20142
4
Discovering the relationship between student effort and ability for predicting the performance of technology-assisted learning in a mathematics after-school program
20130
5 20133
6 20123
7 20111
8 20071
9 20055
10 200532
11 200522
12 2002115
13 200269
14
A word-based soft clustering algorithm for documents.
200113
15 200119
16 200041
17
Rule discovery from time series
1998377
18
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
1995409
19 1995618
20 1995110

About King-Ip Lin

King-Ip Lin is a scholar working on Signal Processing, Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition and Software, having authored 31 papers that have together received 2.2k indexed citations. Recurring topics across this work include Data Management and Algorithms (13 papers), Data Mining Algorithms and Applications (9 papers), Algorithms and Data Compression (6 papers), Advanced Image and Video Retrieval Techniques (5 papers), Advanced Clustering Algorithms Research (5 papers), Advanced Database Systems and Queries (3 papers), Video Analysis and Summarization (3 papers) and Spam and Phishing Detection (2 papers). The work is most often cited by research in Signal Processing (1.3k citations), Computer Vision and Pattern Recognition (805 citations), Artificial Intelligence (813 citations), Information Systems (517 citations) and Computer Graphics and Computer-Aided Design (65 citations). King-Ip Lin has collaborated with scholars based in United States, Canada and Hong Kong. Frequent co-authors include Christos Faloutsos, Kyuseok Shim, Harpreet Sawhney, Rakesh Agrawal, Heikki Mannila, Gautam Das, Padhraic Smyth, Cheng Yang, Vikrant Kobla and David Doermann. Their work appears in journals such as ACM SIGMOD Record, Knowledge and Information Systems, Journal on Computing and Cultural Heritage, Data & Knowledge Engineering and IEEE Transactions on Knowledge and Data Engineering.

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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