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John W. Tukey

dblp:20/3301 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 1992
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Databases, data management, data science and information retrieval · 1Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 75% Data mining · 25%
Artificial intelligence
1 paper
Optimization for machine learning · 100%

Topics — the 5 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › interactive information retrieval › browsing
cluster-based browsing		 0.011992
Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections · SIGIR 1992
Information retrieval › interactive information retrieval › browsing
document browsing		 0.011992
Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections · SIGIR 1992
Data mining › clustering
document clustering		 0.011992
Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections · SIGIR 1992
Information retrieval › document retrieval › interactive document retrieval
scatter/gather		 0.011992
Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections · SIGIR 1992
Machine learning › Optimization for machine learning
projection pursuit		 0.011974
A Projection Pursuit Algorithm for Exploratory Data Analysis · IEEE Trans. Computers 1974

Methods — techniques the papers use, named apart from their topics

document clustering · 0.0projection pursuit · 0.0
YearPublicationVenuePosition
1992 Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections
abstract
Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably improve retrieval.
Douglas R. Cutting, Jan O. Pedersen 0001, David R. Karger, John W. Tukey
 SIGIR4
1974 A Projection Pursuit Algorithm for Exploratory Data Analysis
abstract
An algorithm for the analysis of multivariate data is presented and is discussed in terms of specific examples. The algorithm seeks to find one-and two-dimensional linear projections of multivariate data that are relatively highly revealing.
Jerome H. Friedman, John W. Tukey
 IEEE Trans. Computers2
1966 A practicing statistician looks at the Transactions
John W. Tukey
 IEEE Trans. Inf. Theory1