StreamKrimp: Detecting Change in Data Streams. Data streams are ubiquitous. Examples range from sensor networks to financial transactions and website logs. In fact, even market basket data can be seen as a stream of sales. Detecting changes in the distribution a stream is sampled from is one of the most challenging problems in stream mining, as only limited storage can be used. In this paper we analyse this problem for streams of transaction data from an MDL perspective. Based on this analysis we introduce the StreamKrimp algorithm, whichuses the Krimp algorithm to characterise probability distributions with code tables. With these code tables, StreamKrimp partitions the stream into a sequence of substreams. Each switch of code table indicates a change in the underlying distribution. Experiments on both real and artificial streams show that StreamKrimp detects the changes while using only a very limited amount of data storage.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Trabold, Daniel; Horváth, Tamás; Wrobel, Stefan: Effective approximation of parametrized closure systems over transactional data streams (2020)
- Hess, Sibylle; Morik, Katharina; Piatkowski, Nico: The PRIMPING routine -- tiling through proximal alternating linearized minimization (2017)
- Appice, Annalisa; Guccione, Pietro; Malerba, Donato; Ciampi, Anna: Dealing with temporal and spatial correlations to classify outliers in geophysical data streams (2014)
- Badarna, Murad; Wolff, Ran: Fast and accurate detection of changes in data streams (2014)
- Gama, João; Žliobaitė, Indrė; Bifet, Albert; Pechenizkiy, Mykola; Bouchachia, Abdelhamid: A survey on concept drift adaptation (2014)
- Lam, Hoang Thanh; Mörchen, Fabian; Fradkin, Dmitriy; Calders, Toon: Mining compressing sequential patterns (2014)
- Vreeken, Jilles; Van Leeuwen, Matthijs; Siebes, Arno: Krimp: mining itemsets that compress (2011)
- van Leeuwen, Matthijs; Siebes, Arno: StreamKrimp: Detecting change in data streams (2008) ioport