Matej KloskaKempelen Institute of Intelligent Technologies
Matej Kloska
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Publications (5)
This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern mining, anomaly detection, time series indexing and other data mining tasks. However, the disadvantage of this method is, that it...
This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern mining, anomaly detection, time series indexing and other data mining tasks. However, the disadvantage of this method is, that it...
The Symbolic Aggregate approXimation algorithm (SAX) is one of the most popular symbolic mapping techniques for time series. It is extensively utilized in sequence classification, pattern mining, anomaly detection and many other data mining tasks. SAX as a powerful symbolic mapping technique is widely used due to its data adaptability. However this...
When accessing or manipulating large document corpora, semantics is crucial for enabling machines understand document content and deliver advanced functionality such as recommendation or intelligent search. Manual creation of semantics is a tedious task not sufficiently supported in state-of-the-art tools. In our work we focus on supporting efficie...