Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Daryl J. Eigen

dblp:53/6699 · DBLP profile ↗
← Back
3ranked-venue papers
2as first author
0since 2021 · last 1985
0000-0002-0936-0251ORCID · reported

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

Computer networks · 2 · 1 first-authorHuman-computer interaction and ubiquitous computing · 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.

Computer networks
2 papers
Internet architecture and protocols · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet architecture and protocols
ISDN
0.021985
ISDN architecture: a basis for new services · SIGCOMM 1985
Integrated services digital network (ISDN, panel) · SIGCOMM 1985
Internet architecture and protocols
service integration
0.011985
ISDN architecture: a basis for new services · SIGCOMM 1985
YearPublicationVenuePosition
1985 Integrated services digital network (ISDN, panel)
abstract
No abstract available.
S. Newman, Daryl J. Eigen, A. Clark, J. Mackie, J. Kaufeld
SIGCOMM3
1985 ISDN architecture: a basis for new services
Daryl J. Eigen
SIGCOMM1
1974 Cluster Analysis Based on Dimensional Information with Applications to Feature Selection and Classification
abstract
A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.
Daryl J. Eigen, Frederick R. Fromm, Richard A. Northouse
IEEE Trans. Syst. Man Cybern.1