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ISMIS
2009
Springer
13 years 11 months ago
Novelty Detection from Evolving Complex Data Streams with Time Windows
Abstract. Novelty detection in data stream mining denotes the identification of new or unknown situations in a stream of data elements flowing continuously in at rapid rate. This...
Michelangelo Ceci, Annalisa Appice, Corrado Loglis...
MLDM
2009
Springer
13 years 11 months ago
Relational Frequent Patterns Mining for Novelty Detection from Data Streams
We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at cons...
Michelangelo Ceci, Annalisa Appice, Corrado Loglis...
DIS
2009
Springer
13 years 11 months ago
A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams
Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered...
Fabio Fumarola, Anna Ciampi, Annalisa Appice, Dona...
JCST
2008
175views more  JCST 2008»
13 years 5 months ago
Improved Approximate Detection of Duplicates for Data Streams Over Sliding Windows
Detecting duplicates in data streams is an important problem that has a wide range of applications. In general, precisely detecting duplicates in an unbounded data stream is not fe...
Hong Shen, Yu Zhang
EDBT
2009
ACM
166views Database» more  EDBT 2009»
13 years 9 months ago
Neighbor-based pattern detection for windows over streaming data
The discovery of complex patterns such as clusters, outliers, and associations from huge volumes of streaming data has been recognized as critical for many domains. However, patte...
Di Yang, Elke A. Rundensteiner, Matthew O. Ward