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» Fast data stream algorithms using associative memories
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IDEAL
2003
Springer
14 years 25 days ago
Experiences of Using a Quantitative Approach for Mining Association Rules
In recent years interest has grown in “mining” large databases to extract novel and interesting information. Knowledge Discovery in Databases (KDD) has been recognised as an em...
L. Dong, Christos Tjortjis
EDBT
2008
ACM
135views Database» more  EDBT 2008»
14 years 7 months ago
Minimizing latency and memory in DSMS: a unified approach to quasi-optimal scheduling
Data Stream Management Systems (DSMSs) must support optimized execution scheduling of multiple continuous queries on massive, and frequently bursty, data streams. Previous approac...
Yijian Bai, Carlo Zaniolo
ISCI
2006
58views more  ISCI 2006»
13 years 7 months ago
Streaming data reduction using low-memory factored representations
Many special purpose algorithms exist for extracting information from streaming data. Constraints are imposed on the total memory and on the average processing time per data item....
David Littau, Daniel Boley
JIIS
2006
147views more  JIIS 2006»
13 years 7 months ago
Mining sequential patterns from data streams: a centroid approach
In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In data stream pro...
Alice Marascu, Florent Masseglia
ICDE
1995
IEEE
139views Database» more  ICDE 1995»
14 years 9 months ago
Set-Oriented Mining for Association Rules in Relational Databases
We describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and may appear to be inherently less escient than special-purpose...
Maurice A. W. Houtsma, Arun N. Swami