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» Methods for finding frequent items in data streams
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CIB
2004
57views more  CIB 2004»
13 years 7 months ago
Identifying Global Exceptional Patterns in Multi-database Mining
In multi-database mining, there can be many local patterns (frequent itemsets or association rules) in each database. At the end of multi-database mining, it is necessary to analyz...
Chengqi Zhang, Meiling Liu, Wenlong Nie, Shichao Z...
PVLDB
2008
110views more  PVLDB 2008»
13 years 7 months ago
Online maintenance of very large random samples on flash storage
Recent advances in flash media have made it an attractive alternative for data storage in a wide spectrum of computing devices, such as embedded sensors, mobile phones, PDA's...
Suman Nath, Phillip B. Gibbons
KDD
2007
ACM
184views Data Mining» more  KDD 2007»
14 years 8 months ago
GraphScope: parameter-free mining of large time-evolving graphs
How can we find communities in dynamic networks of social interactions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity timepoints in ...
Jimeng Sun, Christos Faloutsos, Spiros Papadimitri...
KDD
2009
ACM
162views Data Mining» more  KDD 2009»
14 years 8 months ago
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations ...
Mohsen Jamali, Martin Ester
SIGMOD
2008
ACM
138views Database» more  SIGMOD 2008»
14 years 7 months ago
Sampling time-based sliding windows in bounded space
Random sampling is an appealing approach to build synopses of large data streams because random samples can be used for a broad spectrum of analytical tasks. Users are often inter...
Rainer Gemulla, Wolfgang Lehner