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David Clutter

dblp:73/5111 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

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

Databases, data management, data science and information retrieval · 2Artificial intelligence and machine learning · 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
1 paper
Data mining · 82% Data stream processing · 18%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
clustering		 0.012004
MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004
Data mining › clustering › online clustering
data stream clustering		 0.012004
MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004
Data mining › pattern mining
frequent pattern mining		 0.012004
MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004
Data mining
pattern mining		 0.012004
MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004
Data stream processing
stream mining		 0.012004
MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004
Data mining › predictive modeling
classification		 0.012004
MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004
Data mining › predictive modeling › classification › incremental classification
online classification		 0.012004
MAIDS: Mining Alarming Incidents from Data Streams · SIGMOD Conference 2004

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

tilted time window · 0.0data cube · 0.0
YearPublicationVenuePosition
2004 MAIDS: Mining Alarming Incidents from Data Streams
abstract
This paper presents a demonstration of our recent research and development of the MAIDS system, which mines alarming incidents from data streams, with the following major analysis functions: (1) multi-resolution modeling using a tilted time window framework, (2) multi-dimensional analysis using a stream “data cube” model, (3) online stream classification, (4) online frequent pattern mining, (5) online clustering of data streams, and (6) stream mining visualization.
Y. Dora Cai, David Clutter, Greg Pape, Jiawei Han 0001, Michael Welge, Loretta Auvil
 SIGMOD Conference2
2002 High-Performance Commercial Data Mining: A Multistrategy Machine Learning Application
William H. Hsu, Michael Welge, Thomas Redman 0001, David Clutter
 Data Min. Knowl. Discov.4
2000 Genetic Wrappers for Constructive Induction in High-Performance Data Mining
William H. Hsu, Michael Welge, Thomas Redman 0001, David Clutter
 GECCO4