D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 47 Citations 28,819 123 World Ranking 3327 National Ranking 17

Research.com Recognitions

Awards & Achievements

2019 - IEEE Fellow For contributions to scalable data mining and database query processing

2013 - ACM Fellow For contributions to scalable data mining and query processing.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Database
  • Programming language

His primary areas of investigation include Data mining, Query optimization, Cluster analysis, Artificial intelligence and k-medians clustering. His studies deal with areas such as Computation and Data set as well as Data mining. His work carried out in the field of Query optimization brings together such families of science as Web search query, Sargable, Query expansion and Feature.

His Cluster analysis course of study focuses on Categorical variable and Brown clustering, Conceptual clustering, DBSCAN, Clustering high-dimensional data and Biclustering. His Artificial intelligence study combines topics from a wide range of disciplines, such as Offset and Pattern recognition. His k-medians clustering research integrates issues from Data stream clustering, Database and Single-linkage clustering.

His most cited work include:

  • CURE: an efficient clustering algorithm for large databases (1834 citations)
  • Efficient algorithms for mining outliers from large data sets (1514 citations)
  • ROCK: a robust clustering algorithm for categorical attributes (1206 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Data mining, Algorithm, Artificial intelligence, Information retrieval and Query optimization. His Data mining research incorporates elements of Signature and Data set. His study in Algorithm is interdisciplinary in nature, drawing from both Histogram, Theoretical computer science and Joins.

His research integrates issues of Machine learning, Categorical variable and Pattern recognition in his study of Artificial intelligence. His work in Pattern recognition tackles topics such as Cluster analysis which are related to areas like Database. In his study, Web query classification is strongly linked to Query expansion, which falls under the umbrella field of Query optimization.

He most often published in these fields:

  • Data mining (35.14%)
  • Algorithm (18.38%)
  • Artificial intelligence (14.05%)

What were the highlights of his more recent work (between 2017-2021)?

  • The Internet (6.49%)
  • Reverse engineering (3.78%)
  • Algorithm (18.38%)

In recent papers he was focusing on the following fields of study:

Kyuseok Shim mostly deals with The Internet, Reverse engineering, Algorithm, Protocol and Network management. In his study, which falls under the umbrella issue of The Internet, Real-time computing, Identification and Data mining is strongly linked to Signature. The various areas that Kyuseok Shim examines in his Data mining study include Construct and Network attack.

His work on Differential privacy, Space partitioning and Quadtree as part of general Algorithm research is often related to Publication, thus linking different fields of science. The study of Statistical classification is intertwined with the study of Cluster analysis in a number of ways. His Artificial intelligence research includes elements of Categorical variable and Unstructured data.

Between 2017 and 2021, his most popular works were:

  • Protocol Specification Extraction Based on Contiguous Sequential Pattern Algorithm (8 citations)
  • Inference of network unknown protocol structure using CSP(Contiguous Sequence Pattern) algorithm based on tree structure (4 citations)
  • Automatic Payload Signature Generation for Accurate Identification of Internet Applications and Application Services (3 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Database

Kyuseok Shim spends much of his time researching Algorithm, Volume, Network management, The Internet and Internet traffic. His Algorithm research is multidisciplinary, incorporating elements of Representation, Histogram, Interval and Partition. His study in Volume intersects with areas of studies such as Data mining, Construct, Structure, Protocol and Network analysis.

His Network management research incorporates themes from Binary protocol, Signature, Real-time computing and Identification. His work deals with themes such as Telecommunications network, Tree structure, Network security and Reverse engineering, which intersect with The Internet. The Internet traffic study combines topics in areas such as Finite-state machine, Syntax and Protocol specification, Protocol.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Cure: an efficient clustering algorithm for large databases

Sudipto Guha;Rajeev Rastogi;Kyuseok Shim.
Information Systems (2001)

4286 Citations

ROCK: a robust clustering algorithm for categorical attributes

Sudipto Guha;Rajeev Rastogi;Kyuseok Shim.
international conference on data engineering (1999)

2508 Citations

Efficient algorithms for mining outliers from large data sets

Sridhar Ramaswamy;Rajeev Rastogi;Kyuseok Shim.
international conference on management of data (2000)

2244 Citations

Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases

Rakesh Agrawal;King-Ip Lin;Harpreet S. Sawhney;Kyuseok Shim.
very large data bases (1995)

1052 Citations

Approximate Query Processing Using Wavelets

Kaushik Chakrabarti;Minos N. Garofalakis;Rajeev Rastogi;Kyuseok Shim.
very large data bases (2001)

733 Citations

SPIRIT: Sequential Pattern Mining with Regular Expression Constraints

Minos N. Garofalakis;Rajeev Rastogi;Kyuseok Shim.
very large data bases (1999)

719 Citations

Optimizing queries with materialized views

Surajit Chaudhuri;Ravi Krishnamurthy;Spyros Potamianos;Kyuseok Shim.
international conference on data engineering (1995)

638 Citations

APEX: an adaptive path index for XML data

Chin-Wan Chung;Jun-Ki Min;Kyuseok Shim.
international conference on management of data (2002)

481 Citations

PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning

Rajeev Rastogi;Kyuseok Shim.
very large data bases (1998)

401 Citations

Data-streams and histograms

Sudipto Guha;Nick Koudas;Kyuseok Shim.
symposium on the theory of computing (2001)

398 Citations

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