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» Evaluating algorithms that learn from data streams
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118
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ILP
2004
Springer
15 years 3 months ago
Learning an Approximation to Inductive Logic Programming Clause Evaluation
One challenge faced by many Inductive Logic Programming (ILP) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as ...
Frank DiMaio, Jude W. Shavlik
102
Voted
WSDM
2009
ACM
136views Data Mining» more  WSDM 2009»
15 years 5 months ago
Mining common topics from multiple asynchronous text streams
Text streams are becoming more and more ubiquitous, in the forms of news feeds, weblog archives and so on, which result in a large volume of data. An effective way to explore the...
Xiang Wang 0002, Kai Zhang, Xiaoming Jin, Dou Shen
ICDM
2006
IEEE
92views Data Mining» more  ICDM 2006»
15 years 4 months ago
Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams
Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as...
Jimeng Sun, Spiros Papadimitriou, Philip S. Yu
108
Voted
ADC
2007
Springer
193views Database» more  ADC 2007»
15 years 4 months ago
Optimizing XPath Queries on Streaming XML Data
XML stream processing has recently become popular for many applications such as selective dissemination of information. Several approaches have been proposed and most of them are ...
Keerati Jittrawong, Raymond K. Wong
125
Voted
IEAAIE
2009
Springer
15 years 4 months ago
An Efficient Algorithm for Maintaining Frequent Closed Itemsets over Data Stream
Data mining refers to the process of revealing unknown and potentially useful information from a large database. Frequent itemsets mining is one of the foundational problems in dat...
Show-Jane Yen, Yue-Shi Lee, Cheng-Wei Wu, Chin-Lin...