Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Mining data streams with dynamic confidence intervals
 Madria, S.: Big data analytics and knowledge discovery : 18th international conference, DaWaK 2016, Porto, Portugal, September 68, 2016. Proceedings Cham: Springer, 2016 (Lecture Notes in Computer Science 9829) ISBN: 9783319439457 (print) ISBN: 9783319439464 (electronic) pp.99113 
 International Conference on Data Warehousing and Knowledge Discovery (DaWaK) <18 2016, Porto> 

 English 
 Conference Paper 
 Fraunhofer IAIS () 
Abstract
We consider data streams of transactions that are generated independently with some nonstationary 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 realworld 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.