Data for: Non-parametric Sequence-based learning Approach for Outlier Detection in IoT

Published: 23 Nov 2017 | Version 1 | DOI: 10.17632/rb4p5d6xwh.1

Description of this data

The file contains four time varying sensor readings that are the predictor variables Distance (Cm), Humidity (%), Temperature ( C ), Light (Lux). Distance signifies the distance from the closest obstacle. The error for each of the four cases are given as dist_Error, hum_Error, temp_Error and light_Error. Value of 1 indicates it is an error-type outlier. Event response variable is denoted as Event(0 if occupant detected; 1 if not detected). Events are ground truth occupancy data.

Experiment data files

This data is associated with the following publication:

Non-parametric sequence-based learning approach for outlier detection in IoT

Published in: Future Generation Computer Systems

Latest version

  • Version 1


    Published: 2017-11-23

    DOI: 10.17632/rb4p5d6xwh.1

    Cite this dataset

    Nesa, Nashreen (2017), “Data for: Non-parametric Sequence-based learning Approach for Outlier Detection in IoT”, Mendeley Data, v1


Detection Systems

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CC BY NC 3.0 Learn more

The files associated with this dataset are licensed under a Attribution-NonCommercial 3.0 Unported licence.

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You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.