Local Outlier Factor for Feature‐evolving Data Streams: Unterschied zwischen den Versionen
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|betreuer=Florian Kalinke | |betreuer=Florian Kalinke | ||
|termin=Institutsseminar/2021-01-08 | |termin=Institutsseminar/2021-01-08 | ||
|kurzfassung=In high-volume data streams it is often unpractical to monitor all observations | |kurzfassung=In high-volume data streams it is often unpractical to monitor all observations -- often we are only interested in deviations from the normal operation. Detecting outlying observations in data streams is an active area of research. | ||
However, most approaches assume that the data's dimensionality, i.e., the number of attributes, stays constant over time. This assumption is unjustified in many real-world use cases, such as sensor networks or computer cluster monitoring. | However, most approaches assume that the data's dimensionality, i.e., the number of attributes, stays constant over time. This assumption is unjustified in many real-world use cases, such as sensor networks or computer cluster monitoring. | ||
Version vom 4. Januar 2021, 08:42 Uhr
| Vortragende(r) | Elena Schediwie | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Florian Kalinke | |
| Termin | Fr 8. Januar 2021 | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | In high-volume data streams it is often unpractical to monitor all observations -- often we are only interested in deviations from the normal operation. Detecting outlying observations in data streams is an active area of research.
However, most approaches assume that the data's dimensionality, i.e., the number of attributes, stays constant over time. This assumption is unjustified in many real-world use cases, such as sensor networks or computer cluster monitoring. Feature-evolving data streams do not impose this restriction and thereby pose additional challenges. In this thesis, we extend the well-known Local Outlier Factor (LOF) algorithm for outlier detection from the static case to the feature-evolving setting. Our algorithm combines subspace projection techniques with an appropriate index structure using only bounded computational resources. By discarding old observations our approach also deals with concept drift. We evaluate our approach against the respective state-of-the-art methods in the static case, the streaming case, and the feature-evolving case. | |