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COOPIS
2003
IEEE
14 years 3 months ago
Mining for Lexons: Applying Unsupervised Learning Methods to Create Ontology Bases
Ontologies in current computer science parlance are computer based resources that represent agreed domain semantics. This paper first introduces ontologies in general and subseque...
Marie-Laure Reinberger, Peter Spyns, Walter Daelem...
KDD
2004
ACM
125views Data Mining» more  KDD 2004»
14 years 10 months ago
Differential Association Rule Mining for the Study of Protein-Protein Interaction Networks
Protein-protein interactions are of great interest to biologists. A variety of high-throughput techniques have been devised, each of which leads to a separate definition of an int...
Christopher Besemann, Anne Denton, Ajay Yekkirala,...
WEBI
2009
Springer
14 years 4 months ago
Data Mining for Malicious Code Detection and Security Applications
: Data mining is the process of posing queries and extracting patterns, often previously unknown from large quantities of data using pattern matching or other reasoning techniques....
Bhavani M. Thuraisingham
AUSDM
2006
Springer
94views Data Mining» more  AUSDM 2006»
14 years 1 months ago
Marking Time in Sequence Mining
Sequence mining is often conducted over static and temporal datasets as well as over collections of events (episodes). More recently, there has also been a focus on the mining of ...
Carl Mooney, John F. Roddick
VLDB
1998
ACM
103views Database» more  VLDB 1998»
14 years 2 months ago
The Drill Down Benchmark
Data Mining places specific requirements on DBMS query performance that cannot be evaluated satisfactorily using existing OLAP benchmarks. The DD Benchmark - defined here - provid...
Peter A. Boncz, Tim Rühl, Fred Kwakkel