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» Statistical Supports for Frequent Itemsets on Data Streams
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ICANN
2005
Springer
15 years 5 months ago
Principles of Employing a Self-organizing Map as a Frequent Itemset Miner
This work proposes a theoretical guideline in the specific area of Frequent Itemset Mining (FIM). It supports the hypothesis that the use of neural network technology for the prob...
Vicente O. Baez-Monroy, Simon O'Keefe
102
Voted
PAKDD
2010
ACM
208views Data Mining» more  PAKDD 2010»
15 years 1 months ago
Efficient Pattern Mining of Uncertain Data with Sampling
Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic solutions. In the case of uncertain data, however, several new techniques have be...
Toon Calders, Calin Garboni, Bart Goethals
ICDM
2005
IEEE
157views Data Mining» more  ICDM 2005»
15 years 5 months ago
Blocking Anonymity Threats Raised by Frequent Itemset Mining
In this paper we study when the disclosure of data mining results represents, per se, a threat to the anonymity of the individuals recorded in the analyzed database. The novelty o...
Maurizio Atzori, Francesco Bonchi, Fosca Giannotti...
EDBT
2008
ACM
206views Database» more  EDBT 2008»
15 years 11 months ago
Designing an inductive data stream management system: the stream mill experience
There has been much recent interest in on-line data mining. Existing mining algorithms designed for stored data are either not applicable or not effective on data streams, where r...
Hetal Thakkar, Barzan Mozafari, Carlo Zaniolo
CINQ
2004
Springer
125views Database» more  CINQ 2004»
15 years 5 months ago
Deducing Bounds on the Support of Itemsets
Mining Frequent Itemsets is the core operation of many data mining algorithms. This operation however, is very data intensive and sometimes produces a prohibitively large output. I...
Toon Calders