Paper Info
Title
ABS: The Anti Bouncing Model for Usage Data Streams
Abstract
Usage data mining is an important research area with applications in various fields. However, usage data is usually considered streaming, due to its high volumes and rates. Because of these characteristics, we only have access, at any point in time, to a small fraction of the stream. When the data is observed through such a limited window, it is challenging to give a reliable description of the recent usage data. We study the important consequences of these constraints, through the “bounce rate” problem and the clustering of usage data streams. Then, we propose the ABS (Anti-Bouncing Stream) model which combines the advantages of previous models but discards their drawbacks. First, under the same resource constraints as existing models in the literature, ABS can better model the recent data. Second, owing to its simple but effective management approach, the data in ABS is available at any time for analysis. We demonstrate its superiority through a theoretical study and experiments on two real-world data sets.
Year
DOI
Venue
2010
10.1109/ICDM.2010.91
ICDM
Keywords
Field
DocType
previous model,theoretical study,usage data streams,pattern clustering,bounce rate problem,usage,data streams,usage data,recent data,important research area,usage data stream,important consequence,bounce rate,data handling,usage data mining,data streams clustering,real-world data set,anti bouncing model,recent usage data,data stream,clustering,clustering algorithms,mobile communication,data models,navigation,entropy,data mining
Bounce rate,Data modeling,Data mining,Data stream mining,Data set,Data stream,Computer science,Usage data,Cluster analysis,Group method of data handling
Conference
ISSN
ISBN
Citations 
1550-4786 E-ISBN : 978-0-7695-4256-0
978-0-7695-4256-0
4
PageRank 
References 
Authors
0.40
11
3
Name
Order
Citations
PageRank
Chongsheng Zhang1164.05
Florent Masseglia240843.08
Yves Lechevallier333333.02