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A Framework For Spatio-Temporal Data Analysis And Hypothesis Exploration, Alexander Campbell, Binh Pham, Yu-Chu Tian
A Framework For Spatio-Temporal Data Analysis And Hypothesis Exploration, Alexander Campbell, Binh Pham, Yu-Chu Tian
International Congress on Environmental Modelling and Software
We present a general framework for pattern discovery and hypothesis exploration in spatio-temporaldata sets that is based on delay-embedding. This is a remarkable method of nonlinear time-series analysis thatallows the full phase-space behaviour of a system to be reconstructed from only a single observable (accessiblevariable). Recent extensions to the theory that focus on a probabilistic interpretation extend its scope and allowpractical application to noisy, uncertain and high-dimensional systems. Our framework uses these extensions toaid alignment of spatio-temporal sub-models (hypotheses) to empirical data - for example, satellite images plusremote-sensing - and to explore behaviours consistent with this alignment. The novel aspect …
Data Mining Approaches To Explaining Aerosol Formation, Saara Hyvonen, Heikki Junninen, Lauri Laakso, Miikka Dal Maso, Tiia Gronholm, Boris Bonn, Petri Keronen, Pasi Aalto, Veijo Hiltunen, Toivo Pohja, Samuli Launiainen, Pertti Hari, Heikki Mannila, H.C. Hansoon, M. Kulmala
Data Mining Approaches To Explaining Aerosol Formation, Saara Hyvonen, Heikki Junninen, Lauri Laakso, Miikka Dal Maso, Tiia Gronholm, Boris Bonn, Petri Keronen, Pasi Aalto, Veijo Hiltunen, Toivo Pohja, Samuli Launiainen, Pertti Hari, Heikki Mannila, H.C. Hansoon, M. Kulmala
International Congress on Environmental Modelling and Software
Atmospheric aerosol particle formation is frequently observed in various environments. Yet, despite numerous studies, processes behind these so called nucleation events remain unclear. In this work we describe the use of data mining techniques to detect factors influencing particle formation. These techniques are applied to a dataset of eight years of 80 variables collected at the boreal forest station (SMEAR II) in Southern Finland, including air pollutant, weather, gas and particle measurements. In a previous study classification methods have been used together with feature selection in order to understand what causes nucleation. Each day was classified as an event day, …
Data Mining And Image Segmentation Approaches For Classifying Defoliation In Aerial Forest Imagery, K. Fukuda, P. A. Pearson
Data Mining And Image Segmentation Approaches For Classifying Defoliation In Aerial Forest Imagery, K. Fukuda, P. A. Pearson
International Congress on Environmental Modelling and Software
Experimental data mining and image segmentation approaches are developed to add insight towards aerial image interpretation for defoliation survey procedures. A decision tree classifier generated with a data mining package, WEKA [Witten and Frank, 2005], based on the contents of a small number of training data points, identified from known classes, is used to predict the extents of regions containing different levels of tree mortality (severe, moderate, light and non attack) and land cover (vegetation and ground surface). This approach is applicable to low quality imagery without traditional image pre-processing (e.g., normalization or noise reduction). To generate the decision tree, …
Data Mining As A Tool For Environmental Scientists, J. M. Spate, Karina Gibert, Miquel Sànchez-Marrè, E. Frank, Joaquim Comas, Ioannis N. Athanasiadis
Data Mining As A Tool For Environmental Scientists, J. M. Spate, Karina Gibert, Miquel Sànchez-Marrè, E. Frank, Joaquim Comas, Ioannis N. Athanasiadis
International Congress on Environmental Modelling and Software
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data …
A Framework For Spatio-Temporal Data Analysis And Hypothesis Exploration, Alexander Campbell, Binh Pham, Yu-Chu Tian
A Framework For Spatio-Temporal Data Analysis And Hypothesis Exploration, Alexander Campbell, Binh Pham, Yu-Chu Tian
International Congress on Environmental Modelling and Software
We present a general framework for pattern discovery and hypothesis exploration in spatio-temporaldata sets that is based on delay-embedding. This is a remarkable method of nonlinear time-series analysis thatallows the full phase-space behaviour of a system to be reconstructed from only a single observable (accessiblevariable). Recent extensions to the theory that focus on a probabilistic interpretation extend its scope and allowpractical application to noisy, uncertain and high-dimensional systems. Our framework uses these extensions toaid alignment of spatio-temporal sub-models (hypotheses) to empirical data - for example, satellite images plusremote-sensing - and to explore behaviours consistent with this alignment. The novel aspect …
Data Mining Approaches To Explaining Aerosol Formation, Saara Hyvonen, Heikki Junninen, Lauri Laakso, Miikka Dal Maso, Tiia Gronholm, Boris Bonn, Petri Keronen, Pasi Aalto, Veijo Hiltunen, Toivo Pohja, Samuli Launiainen, Pertti Hari, Heikki Mannila, H.C. Hansoon, M. Kulmala
Data Mining Approaches To Explaining Aerosol Formation, Saara Hyvonen, Heikki Junninen, Lauri Laakso, Miikka Dal Maso, Tiia Gronholm, Boris Bonn, Petri Keronen, Pasi Aalto, Veijo Hiltunen, Toivo Pohja, Samuli Launiainen, Pertti Hari, Heikki Mannila, H.C. Hansoon, M. Kulmala
International Congress on Environmental Modelling and Software
Atmospheric aerosol particle formation is frequently observed in various environments. Yet, despite numerous studies, processes behind these so called nucleation events remain unclear. In this work we describe the use of data mining techniques to detect factors influencing particle formation. These techniques are applied to a dataset of eight years of 80 variables collected at the boreal forest station (SMEAR II) in Southern Finland, including air pollutant, weather, gas and particle measurements. In a previous study classification methods have been used together with feature selection in order to understand what causes nucleation. Each day was classified as an event day, …
Data Mining And Image Segmentation Approaches For Classifying Defoliation In Aerial Forest Imagery, K. Fukuda, P. A. Pearson
Data Mining And Image Segmentation Approaches For Classifying Defoliation In Aerial Forest Imagery, K. Fukuda, P. A. Pearson
International Congress on Environmental Modelling and Software
Experimental data mining and image segmentation approaches are developed to add insight towards aerial image interpretation for defoliation survey procedures. A decision tree classifier generated with a data mining package, WEKA [Witten and Frank, 2005], based on the contents of a small number of training data points, identified from known classes, is used to predict the extents of regions containing different levels of tree mortality (severe, moderate, light and non attack) and land cover (vegetation and ground surface). This approach is applicable to low quality imagery without traditional image pre-processing (e.g., normalization or noise reduction). To generate the decision tree, …
Data Mining As A Tool For Environmental Scientists, J. M. Spate, Karina Gibert, Miquel Sànchez-Marrè, E. Frank, Joaquim Comas, Ioannis N. Athanasiadis
Data Mining As A Tool For Environmental Scientists, J. M. Spate, Karina Gibert, Miquel Sànchez-Marrè, E. Frank, Joaquim Comas, Ioannis N. Athanasiadis
International Congress on Environmental Modelling and Software
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data …
Temporal Data Mining In A Dynamic Feature Space, Brent K. Wenerstrom
Temporal Data Mining In A Dynamic Feature Space, Brent K. Wenerstrom
Theses and Dissertations
Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a similar incremental learning algorithm.