Outlier Detection for Data Streams: Data Streams dont wait for processing - Shiblee Sadik
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Piegāde 12-18 darba dienu laikā
30 dienu atgriešanas politika
Outlier detection is a well established area of study for statistical data. However, most of the existing outlier detection techniques are designed targeting the regular data model, where the entire dataset is available for random access. Typical outlier detection techniques construct a standard data distribution or model from the entire dataset and execute their detection algorithms over each data point. E ... Pilns apraksts
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Aprašymas
Outlier detection is a well established area of study for statistical data. However, most of the existing outlier detection techniques are designed targeting the regular data model, where the entire dataset is available for random access. Typical outlier detection techniques construct a standard data distribution or model from the entire dataset and execute their detection algorithms over each data point. Evidently these techniques are not suitable for online data streams where the entire dataset, due to its unbounded volume, is not available for random access. Moreover, the data distribution in data streams change over time which challenges the existing outlier detection techniques that assume a constant standard data distribution for the entire dataset. In addition, data streams are characterized by uncertainty which imposes further complexity. In this work we propose two outlier detection techniques, called Distance Based Outline Detection for Data Streams (DB-ODDS) and Automatic Outlier Detection for Data Streams (A-ODDS). Both techniques are based on a novel continuously adaptive data distribution function that addresses all the issues of data streams.
Vairāk informācijas
| Autors | Shiblee Sadik |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2011 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783846591222 |