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» Mining Multiple Large Databases
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110
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IDEAL
2003
Springer
15 years 7 months 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
JCP
2008
171views more  JCP 2008»
15 years 1 months ago
Mining Frequent Subgraph by Incidence Matrix Normalization
Existing frequent subgraph mining algorithms can operate efficiently on graphs that are sparse, have vertices with low and bounded degrees, and contain welllabeled vertices and edg...
Jia Wu, Ling Chen
99
Voted
VLDB
2004
ACM
94views Database» more  VLDB 2004»
15 years 7 months ago
Whither Data Mining?
The last decade has witnessed tremendous advances in data mining. We take a retrospective look at these developments, focusing on association rules discovery, and discuss the chal...
Rakesh Agrawal, Ramakrishnan Srikant
123
Voted
SIGMOD
1998
ACM
233views Database» more  SIGMOD 1998»
15 years 6 months ago
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications
Data mining applications place special requirements on clustering algorithms including: the ability to nd clusters embedded in subspaces of high dimensional data, scalability, end...
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopul...
PKDD
2009
Springer
134views Data Mining» more  PKDD 2009»
15 years 8 months ago
Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)
Abstract. We address the problem of joint feature selection in multiple related classification or regression tasks. When doing feature selection with multiple tasks, usually one c...
Paramveer S. Dhillon, Brian Tomasik, Dean P. Foste...