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» Methods for finding frequent items in data streams
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HIS
2004
13 years 9 months ago
Hybrid Learning Scheme for Data Mining Applications
Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directl...
T. Ravindra Babu, M. Narasimha Murty, Vijay K. Agr...
TAMC
2009
Springer
14 years 2 months ago
Preserving Privacy versus Data Retention
The retention of communication data has recently attracted much public interest, mostly because of the possibility of its misuse. In this paper, we present protocols that address ...
Markus Hinkelmann, Andreas Jakoby
DMIN
2007
158views Data Mining» more  DMIN 2007»
13 years 9 months ago
Mining Frequent Itemsets Using Re-Usable Data Structure
- Several algorithms have been introduced for mining frequent itemsets. The recent datasettransformation approach suffers either from the possible increasing in the number of struc...
Mohamed Yakout, Alaaeldin M. Hafez, Hussein Aly
SIGMOD
2005
ACM
106views Database» more  SIGMOD 2005»
14 years 7 months ago
Tributaries and Deltas: Efficient and Robust Aggregation in Sensor Network Streams
Existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, tree-based and multi-path-based, with each having unique s...
Amit Manjhi, Suman Nath, Phillip B. Gibbons
PARMA
2004
191views Database» more  PARMA 2004»
13 years 9 months ago
Identifying Most Predictive Items
Abstract. Frequent itemsets and association rules are generally accepted concepts in analyzing item-based databases. The Apriori-framework was developed for analyzing categorical d...
Markus Wawryniuk, Daniel A. Keim