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Robert Zembowicz

dblp:19/2401 · DBLP profile ↗
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9ranked-venue papers
3as first author
0since 2021 · last 1997
—ORCID · none

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

Artificial intelligence and machine learning · 7 · 3 first-authorDatabases, data management, data science and information retrieval · 2Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorTheory of computation · 1

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
2 papers
Data mining · 85% Machine learning and data management · 15%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%
Theoretical computer science
1 paper
Automated reasoning and model checking · 100%

Topics — the 2 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
pattern mining
0.011996
Automated Pattern Mining with a Scale Dimension · KDD 1996
Computational science and engineering › scientific machine learning
equation discovery
0.011992
Discovery of Equations: Experimental Evaluation of Convergence · AAAI 1992

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

experimental evaluation · 0.0
YearPublicationVenuePosition
1997 Contigency Tables as the Foundation for Concepts, Concept Hierarchies and Rules: The 49er System Approach
abstract
We analyze relationships between different forms of knowledge that can be discovered in the same data matrix (relational table): contingency tables, equations, concept definitions, concept hierarchies, and rules. We argue that contingency tables are
Jan M. Zytkow, Robert Zembowicz
Fundam. Informaticae2
1996 Search for Patterns at Each Scale in Massive Data
Jan M. Zytkow, Robert Zembowicz
ISMIS2
1996 Automated Pattern Mining with a Scale Dimension
Jan M. Zytkow, Robert Zembowicz
KDD2
1993 Recognition of Functional Dependencies in Data
Robert Zembowicz, Jan M. Zytkow
ISMIS1
1993 Database Exploration in Search of Regularities
Jan M. Zytkow, Robert Zembowicz
J. Intell. Inf. Syst.2
1992 Discovery of Equations: Experimental Evaluation of Convergence
Robert Zembowicz, Jan M. Zytkow
AAAI1
1992 Operational Definition Refinement: A Discovery Process
Jan M. Zytkow, Jieming Zhu, Robert Zembowicz
AAAI3
1992 The First Phase of Real-World Discovery: Determining Repeatability and Error of Experiments
Jan M. Zytkow, Jieming Zhu, Robert Zembowicz
ML3
1991 Automated Discovery of Empirical Equations from Data
Robert Zembowicz, Jan M. Zytkow
ISMIS1