Outlier Detection and Classification
|A short description
The distribution package
Authors & Aknowledgement
Anomaly detection and change analysis are challenging tasks in stream data mining. We have developed a method that addresses both these tasks in geophysical applications. The method, called SWOD (Sliding Window Outlier Detection), is designed for numeric data routinely sampled through a sensor network. It extends the traditional time series forecasting theory by accounting for the spatial information of geophysical data. In particular, a forecasting model is computed incrementally by accounting for the temporal correlation of data which exhibit a spatial correlation in the recent past. For each sensor the observed value is compared to its spatial-aware forecast, in order to identify the outliers. Finally, the spatial correlation of outliers is analyzed, in order to classify changes and reduce the number of false anomalies.
SWOD is implemented in a Java system. It iterfaces MySQL database to read the network structure (nodes and arcs).
|SWOD||This rar bundle contains (1) swod.jar that allows us to :(i) perform incrementally the discovery of trend clusters over sliding windows, as well as (ii) detect and classify outliers in geophysical data streams (2) setup files and (3) a benchmark data steam (Intel Berkeley Temeprature)|
Warning: SWOD is free for evaluation, research and teaching purposes, but not for commercial purposes.
Annalisa Appice, Pietro Guccione, Donato Malerba, Anna Ciampi
Dealing with temporal and spatial correlations to classify outliers in geophysical data streams. Information Sciences 285: 162-180
|Name||Email address||Tel. number||Fax|
|Annalisa Appicefirstname.lastname@example.org||+39 080 5443262||+39 080 5443262|
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The 19th International Conference on Discovery Science (DS 2016) will be held in Bari on October 2016, 19th-21st. KDDE Group is organizing it.
Bari, Italy, 19-21 October, 2016.