Unsupervised Learning of Spatio-temporal Primitives of Emotional Gait

2006 ◽  
pp. 188-192 ◽  
Author(s):  
Lars Omlor ◽  
Martin A. Giese
Keyword(s):  
Unsupervised Learning ◽  
Spatio Temporal

scholarly journals Mining RFID Behavior Data using Unsupervised Learning

2010 ◽  
Vol 1 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Guénaël Cabanes ◽  
Younès Bennani ◽  
Dominique Fresneau
Keyword(s):  
Data Mining ◽  
Unsupervised Learning ◽  
Radio Frequency Identification ◽  
Spatial Organization ◽  
Self Organizing Map ◽  
Automatic Data ◽  
Frequency Identification ◽  
Spatio Temporal ◽  
Tracking Technology ◽  
Learning Data

Radio Frequency IDentification (RFID) is an advanced tracking technology that can be used to study the spatial organization of individual’s spatio-temporal activity. The aim of this work is firstly to build a new RFID-based autonomous system which can follow individuals’ spatio-temporal activity, a tool not currently available. Secondly, the authors aim to develop new tools for automatic data mining. In this paper, they study how to transform these data to investigate the division of labor, the intra-colonial cooperation and conflict in an ant colony. They also develop a new unsupervised learning data mining method (DS2L-SOM: Density based Simultaneous Two-Level - Self Organizing Map) to find homogeneous clusters (i.e., sets of individual which share a similar behavior). According to the experimental results, this method is very fast and efficient. It also allows a very useful visualization of the results.

Integrated Reservoir Modelling using Spatio-Temporal Unsupervised Learning and Integrated Visualization

2019 ◽  
Author(s):  
Srungeer Simha ◽  
Pradeep Tummala ◽  
Viren Kumar ◽  
Manu Singhal ◽  
Michelle Viswanathan ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Reservoir Modelling ◽  
Spatio Temporal

scholarly journals Mining RFID Behavior Data using Unsupervised Learning

2012 ◽  
pp. 28-48
Author(s):  
Guénaël Cabanes ◽  
Younès Bennani ◽  
Dominique Fresneau
Keyword(s):  
Data Mining ◽  
Unsupervised Learning ◽  
Radio Frequency Identification ◽  
Spatial Organization ◽  
Self Organizing Map ◽  
Automatic Data ◽  
Frequency Identification ◽  
Spatio Temporal ◽  
Tracking Technology ◽  
Learning Data

Radio Frequency IDentification (RFID) is an advanced tracking technology that can be used to study the spatial organization of individual’s spatio-temporal activity. The aim of this work is firstly to build a new RFID-based autonomous system which can follow individuals’ spatio-temporal activity, a tool not currently available. Secondly, the authors aim to develop new tools for automatic data mining. In this paper, they study how to transform these data to investigate the division of labor, the intra-colonial cooperation and conflict in an ant colony. They also develop a new unsupervised learning data mining method (DS2L-SOM: Density-based Simultaneous Two-Level - Self Organizing Map) to find homogeneous clusters (i.e., sets of individual which share a similar behavior). According to the experimental results, this method is very fast and efficient. It also allows a very useful visualization of the results.

Advanced Exploratory Analysis of Air Pollution Multivariate Spatio-Temporal Data

2020 ◽  
Author(s):  
Mikhail Kanevski ◽  
Federico Amato ◽  
Fabian Guignard
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Time Series ◽  
Unsupervised Learning ◽  
Multivariate Time Series ◽  
Clustering Algorithms ◽  
Monitoring Network ◽  
Fractal Dimensions ◽  
First Case ◽  
Spatio Temporal

<p>The research deals with an application of advanced exploratory tools to study hourly spatio-temporal air pollution data collected by NABEL monitoring network in Switzerland. Data analyzed consist of several pollutants, mainly NO2, O3, PM2.5, measured during last two years at 16 stations distributed over the country. The data are considered in two different ways: 1) as multivariate time series measured at the same station (different pollutants and environmental variables, like temperature), 2) as a spatially distributed time series of the same pollutant. In the first case, it is interesting to study both univariate and multivariate time series and their complexity. In the second case, similarity between time series distributed in space can signify the similar underlying phenomena and environmental conditions giving rise to the pollution. An important aspect of the data is that they are collected at the places of different land use classes – urban, suburban, rural etc., which helps in understanding and interpretation of the results.</p><p>Nowadays, unsupervised learning algorithms are widely applied in intelligent exploratory data analysis. Well known tasks of unsupervised learning include manifold learning, dimensionality reduction and clustering. In the present research, intrinsic and fractal dimensions, measures characterizing the similarity and redundancy in data and machine learning clustering algorithms were adapted and applied. The results obtained give a new and important information on the air pollution spatio-temporal patterns. The following results, between others, can be mentioned: 1) some measures of similarity (e.g., complexity-independent distance) are efficient in discriminating between time series; 2) intrinsic dimension, characterizing the ensemble of monitoring data, is pollutant dependent; 3) clustering of time series observed can be interpreted using the available information on land use.  </p>

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