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128
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EMS
2008
IEEE
15 years 10 months ago
A Weighted Utility Framework for Mining Association Rules
Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by assuming that all items have the same significance and frequency of oc...
M. Sulaiman Khan, Maybin K. Muyeba, Frans Coenen
KDD
2000
ACM
149views Data Mining» more  KDD 2000»
15 years 7 months ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar
DAWAK
2006
Springer
15 years 7 months ago
An Approximate Approach for Mining Recently Frequent Itemsets from Data Streams
Recently, the data stream, which is an unbounded sequence of data elements generated at a rapid rate, provides a dynamic environment for collecting data sources. It is likely that ...
Jia-Ling Koh, Shu-Ning Shin
141
Voted
FIMI
2004
175views Data Mining» more  FIMI 2004»
15 years 5 months ago
CT-PRO: A Bottom-Up Non Recursive Frequent Itemset Mining Algorithm Using Compressed FP-Tree Data Structure
Frequent itemset mining (FIM) is an essential part of association rules mining. Its application for other data mining tasks has also been recognized. It has been an active researc...
Yudho Giri Sucahyo, Raj P. Gopalan
DMKD
2000
ACM
110views Data Mining» more  DMKD 2000»
15 years 8 months ago
Combining Strategies for Extracting Relations from Text Collections
Text documents often contain valuable structured data that is hidden in regular English sentences. This data is best exploited if available as a relational table that we could use...
Eugene Agichtein, Eleazar Eskin, Luis Gravano