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» A Sliding Window Algorithm for Relational Frequent Patterns ...
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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...
AUSDM
2007
Springer
145views Data Mining» more  AUSDM 2007»
13 years 11 months ago
Discovering Frequent Sets from Data Streams with CPU Constraint
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredictable rates, and fast changing data characteristics. It has been hence recogniz...
Xuan Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vince...
KDD
2010
ACM
300views Data Mining» more  KDD 2010»
13 years 8 months ago
Mining top-k frequent items in a data stream with flexible sliding windows
We study the problem of finding the k most frequent items in a stream of items for the recently proposed max-frequency measure. Based on the properties of an item, the maxfrequen...
Hoang Thanh Lam, Toon Calders
KDD
2008
ACM
239views Data Mining» more  KDD 2008»
14 years 5 months ago
Mining adaptively frequent closed unlabeled rooted trees in data streams
Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. We propose a new approach for mining closed unlabeled rooted trees a...
Albert Bifet, Ricard Gavaldà
CIKM
2009
Springer
13 years 9 months ago
Efficient itemset generator discovery over a stream sliding window
Mining generator patterns has raised great research interest in recent years. The main purpose of mining itemset generators is that they can form equivalence classes together with...
Chuancong Gao, Jianyong Wang