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  4. Mining data streams with dynamic confidence intervals
 
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2016
Conference Paper
Title

Mining data streams with dynamic confidence intervals

Abstract
We consider data streams of transactions that are generated independently with some non-stationary distribution and regard an itemset to be interesting if its average success probability in the data stream reaches a user specified threshold. We propose an algorithm approximating the family of all interesting itemsets in a data stream. Using Chernoff bounds, our algorithm dynamically adjusts the confidence intervals of the candidate itemsetsâ probabilities. Though the method proposed assumes the itemsets to be independent Poisson trials, our extensive empirical evaluations on synthetic and real-world benchmark datasets clearly demonstrate that it can be applied also to frequent itemset mining from data streams. In addition, the transactions are not necessarily independent.
Author(s)
Trabold, Daniel  
Horvath, Tamas  
Mainwork
Big data analytics and knowledge discovery  
Conference
International Conference on Data Warehousing and Knowledge Discovery (DaWaK) 182016  
DOI
10.1007/978-3-319-43946-4_7
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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