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SIGKDD
2000
237views more  SIGKDD 2000»
13 years 5 months ago
The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation
Advances in data collection and storage have allowed organizations to create massive, complex and heterogeneous databases, which have stymied traditional methods of data analysis....
Stephen D. Bay, Dennis F. Kibler, Michael J. Pazza...
PAKDD
2005
ACM
161views Data Mining» more  PAKDD 2005»
13 years 11 months ago
Online Algorithms for Mining Inter-stream Associations from Large Sensor Networks
We study the problem of mining frequent value sets from a large sensor network. We discuss how sensor stream data could be represented that facilitates efficient online mining and ...
K. K. Loo, Ivy Tong, Ben Kao
SIGMOD
1993
ACM
134views Database» more  SIGMOD 1993»
13 years 9 months ago
Mining Association Rules between Sets of Items in Large Databases
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an e cient algorithm that generates all s...
Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami
KDD
2001
ACM
253views Data Mining» more  KDD 2001»
14 years 5 months ago
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a dis...
Jens-Peter Dittrich, Bernhard Seeger
KDD
2006
ACM
160views Data Mining» more  KDD 2006»
14 years 5 months ago
Coherent closed quasi-clique discovery from large dense graph databases
Frequent coherent subgraphscan provide valuable knowledgeabout the underlying internal structure of a graph database, and mining frequently occurring coherent subgraphs from large...
Zhiping Zeng, Jianyong Wang, Lizhu Zhou, George Ka...