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

Published: 23 Nov 2017 | Version 1 | DOI: 10.17632/rb4p5d6xwh.1
Contributor(s):

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

    2017-11-23

    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 http://dx.doi.org/10.17632/rb4p5d6xwh.1

Categories

Detection Systems

Mendeley Library

Organise your research assets using Mendeley Library. Add to Mendeley Library

Licence

CC BY NC 3.0 Learn more

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

What does this mean?

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

Report