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» Methods for finding frequent items in data streams
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CIKM
2006
Springer
13 years 11 months ago
Finding highly correlated pairs efficiently with powerful pruning
We consider the problem of finding highly correlated pairs in a large data set. That is, given a threshold not too small, we wish to report all the pairs of items (or binary attri...
Jian Zhang, Joan Feigenbaum
KDD
2002
ACM
144views Data Mining» more  KDD 2002»
14 years 8 months ago
Efficiently mining frequent trees in a forest
Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semi-structured data, and so on. We formulate the problem of mining (embedded) subtrees in ...
Mohammed Javeed Zaki
BMCBI
2006
202views more  BMCBI 2006»
13 years 7 months ago
Spectral embedding finds meaningful (relevant) structure in image and microarray data
Background: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing ...
Brandon W. Higgs, Jennifer W. Weller, Jeffrey L. S...
KDD
2006
ACM
143views Data Mining» more  KDD 2006»
14 years 8 months ago
Algorithms for discovering bucket orders from data
Ordering and ranking items of different types are important tasks in various applications, such as query processing and scientific data mining. A total order for the items can be ...
Aristides Gionis, Heikki Mannila, Kai Puolamä...
PVLDB
2008
107views more  PVLDB 2008»
13 years 7 months ago
Finding relevant patterns in bursty sequences
Sequence data is ubiquitous and finding frequent sequences in a large database is one of the most common problems when analyzing sequence data. Unfortunately many sources of seque...
Alexander Lachmann, Mirek Riedewald