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160
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DAWAK
2010
Springer
15 years 4 months ago
Mining Closed Itemsets in Data Stream Using Formal Concept Analysis
Mining of frequent closed itemsets has been shown to be more efficient than mining frequent itemsets for generating non-redundant association rules. The task is challenging in data...
Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar
156
Voted
DATAMINE
2006
230views more  DATAMINE 2006»
15 years 3 months ago
Mining top-K frequent itemsets from data streams
Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to ...
Raymond Chi-Wing Wong, Ada Wai-Chee Fu
173
Voted
EDBT
2009
ACM
137views Database» more  EDBT 2009»
15 years 1 months ago
Flexible and scalable storage management for data-intensive stream processing
Data Stream Management Systems (DSMS) operate under strict performance requirements. Key to meeting such requirements is to efficiently handle time-critical tasks such as managing...
Irina Botan, Gustavo Alonso, Peter M. Fischer, Don...
206
Voted
BTW
2011
Springer
218views Database» more  BTW 2011»
14 years 7 months ago
Tracking Hot-k Items over Web 2.0 Streams
Abstract: The rise of the Web 2.0 has made content publishing easier than ever. Yesterday’s passive consumers are now active users who generate and contribute new data to the web...
Parisa Haghani, Sebastian Michel, Karl Aberer
175
Voted
JMLR
2012
13 years 6 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee