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» A Weighted Utility Framework for Mining Association Rules
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DIS
2004
Springer
14 years 2 months ago
Mining Noisy Data Streams via a Discriminative Model
The two main challenges typically associated with mining data streams are concept drift and data contamination. To address these challenges, we seek learning techniques and models ...
Fang Chu, Yizhou Wang, Carlo Zaniolo
DAWAK
1999
Springer
14 years 1 months ago
Modeling KDD Processes within the Inductive Database Framework
One of the most challenging problems in data manipulation in the future is to be able to e ciently handle very large databases but also multiple induced properties or generalizatio...
Jean-François Boulicaut, Mika Klemettinen, ...
MM
2004
ACM
142views Multimedia» more  MM 2004»
14 years 2 months ago
Learning query-class dependent weights in automatic video retrieval
Combining retrieval results from multiple modalities plays a crucial role for video retrieval systems, especially for automatic video retrieval systems without any user feedback a...
Rong Yan, Jun Yang 0003, Alexander G. Hauptmann
RAID
1999
Springer
14 years 1 months ago
Combining Knowledge Discovery and Knowledge Engineering to Build IDSs
We have been developing a data mining (i.e., knowledge discovery) framework, MADAM ID, for Mining Audit Data for Automated Models for Intrusion Detection [LSM98, LSM99b, LSM99a]. ...
Wenke Lee, Salvatore J. Stolfo
CIKM
2001
Springer
14 years 18 days ago
Sliding-Window Filtering: An Efficient Algorithm for Incremental Mining
We explore in this paper an effective sliding-window filtering (abbreviatedly as SWF) algorithm for incremental mining of association rules. In essence, by partitioning a transact...
Chang-Hung Lee, Cheng-Ru Lin, Ming-Syan Chen