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» Association Rules Discovery in Multivariate Time Series
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ADC
2003
Springer
182views Database» more  ADC 2003»
13 years 11 months ago
CT-ITL : Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth
Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent item sets is computationally the most expens...
Yudho Giri Sucahyo, Raj P. Gopalan
HIPC
2003
Springer
13 years 11 months ago
Parallel and Distributed Frequent Itemset Mining on Dynamic Datasets
Traditional methods for data mining typically make the assumption that data is centralized and static. This assumption is no longer tenable. Such methods waste computational and I/...
Adriano Veloso, Matthew Eric Otey, Srinivasan Part...
RECOMB
2002
Springer
14 years 6 months ago
Monotony of surprise and large-scale quest for unusual words
The problem of characterizing and detecting recurrent sequence patterns such as substrings or motifs and related associations or rules is variously pursued in order to compress da...
Alberto Apostolico, Mary Ellen Bock, Stefano Lonar...
SDM
2009
SIAM
170views Data Mining» more  SDM 2009»
14 years 2 months ago
Mining Complex Spatio-Temporal Sequence Patterns.
Mining sequential movement patterns describing group behaviour in potentially streaming spatio-temporal data sets is a challenging problem. Movements are typically noisy and often...
Florian Verhein