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» Methods for finding frequent items in data streams
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VLDB
2002
ACM
137views Database» more  VLDB 2002»
13 years 7 months ago
Comparing Data Streams Using Hamming Norms (How to Zero In)
Massive data streams are now fundamental to many data processing applications. For example, Internet routers produce large scale diagnostic data streams. Such streams are rarely s...
Graham Cormode, Mayur Datar, Piotr Indyk, S. Muthu...
TKDE
2008
153views more  TKDE 2008»
13 years 7 months ago
Discovering Frequent Agreement Subtrees from Phylogenetic Data
We study a new data mining problem concerning the discovery of frequent agreement subtrees (FASTs) from a set of phylogenetic trees. A phylogenetic tree, or phylogeny, is an unorde...
Sen Zhang, Jason Tsong-Li Wang
SDM
2003
SIAM
134views Data Mining» more  SDM 2003»
13 years 9 months ago
Hierarchical Document Clustering using Frequent Itemsets
A major challenge in document clustering is the extremely high dimensionality. For example, the vocabulary for a document set can easily be thousands of words. On the other hand, ...
Benjamin C. M. Fung, Ke Wang, Martin Ester
ICDM
2009
IEEE
109views Data Mining» more  ICDM 2009»
14 years 2 months ago
Finding Associations and Computing Similarity via Biased Pair Sampling
Sampling-based methods have previously been proposed for the problem of finding interesting associations in data, even for low-support items. While these methods do not guarantee ...
Andrea Campagna, Rasmus Pagh
JCP
2006
139views more  JCP 2006»
13 years 7 months ago
Generalized Sequential Pattern Mining with Item Intervals
Sequential pattern mining is an important data mining method with broad applications that can extract frequent sequences while maintaining their order. However, it is important to ...
Yu Hirate, Hayato Yamana