39,203 research outputs found
Data mining as a tool for environmental scientists
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 mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Complex computational models are often designed to simulate real-world
physical phenomena in many scientific disciplines. However, these simulation
models tend to be computationally very expensive and involve a large number of
simulation input parameters which need to be analyzed and properly calibrated
before the models can be applied for real scientific studies. We propose a
visual analysis system to facilitate interactive exploratory analysis of
high-dimensional input parameter space for a complex yeast cell polarization
simulation. The proposed system can assist the computational biologists, who
designed the simulation model, to visually calibrate the input parameters by
modifying the parameter values and immediately visualizing the predicted
simulation outcome without having the need to run the original expensive
simulation for every instance. Our proposed visual analysis system is driven by
a trained neural network-based surrogate model as the backend analysis
framework. Surrogate models are widely used in the field of simulation sciences
to efficiently analyze computationally expensive simulation models. In this
work, we demonstrate the advantage of using neural networks as surrogate models
for visual analysis by incorporating some of the recent advances in the field
of uncertainty quantification, interpretability and explainability of neural
network-based models. We utilize the trained network to perform interactive
parameter sensitivity analysis of the original simulation at multiple
levels-of-detail as well as recommend optimal parameter configurations using
the activation maximization framework of neural networks. We also facilitate
detail analysis of the trained network to extract useful insights about the
simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns
This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for
exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM)
implements SOM-type learning to one-dimensional arrays for individual time
units, preserves the orientation with short-term memory and arranges the arrays
in an ascending order of time. The two-dimensional representation of the SOTM
attempts thus twofold topology preservation, where the horizontal direction
preserves time topology and the vertical direction data topology. This enables
discovering the occurrence and exploring the properties of temporal structural
changes in data. For representing qualities and properties of SOTMs, we adapt
measures and visualizations from the standard SOM paradigm, as well as
introduce a measure of temporal structural changes. The functioning of the
SOTM, and its visualizations and quality and property measures, are illustrated
on artificial toy data. The usefulness of the SOTM in a real-world setting is
shown on poverty, welfare and development indicators
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Making hurricane track data accessible
Our interactive tool allows the exploration, validation and presentation of hundreds of years of dynamically simulated storm tracks. The tracks were generated as part of a research project to improve the risk assessment of tropical storm damage by the insurance industry. The main impact of the tool is that exploratory interactive visualisation is now being used by the storm track modellers to (a) validate and improve model outputs, (b) discuss outputs with their peers (c) obtain a better understanding of the formation and development of tropical storms and (d) present examples of the behaviour of storms under different conditions to the insurance industry and others. Insights into tropical storm behaviour have been obtained and these insights are being articulated
Assessing clustering methods for exploratory spatial data analysis
Exploratory spatial data analysis continues to be an important area of research. The use and application of clustering methods for the analysis of spatially referenced data is beginning to show some promise. However, a variety of clustering methods does exist. It is essential that a better understanding of these approaches in the geographic domain be pursued in terms of data requirements, computational efficiencies and inherent biases. This paper presents an initial attempt to demonstrate strengths and weaknesses of various clustering approaches for exploratory spatial data analysis.
Dynamic Construction of Stimulus Values in the Ventromedial Prefrontal Cortex
Signals representing the value assigned to stimuli at the time of choice have been repeatedly observed in ventromedial prefrontal cortex (vmPFC). Yet it remains unknown how these value representations are computed from sensory and memory representations in more posterior brain regions. We used electroencephalography (EEG) while subjects evaluated appetitive and aversive food items to study how event-related responses modulated by stimulus value evolve over time. We found that value-related activity shifted from posterior to anterior, and from parietal to central to frontal sensors, across three major time windows after stimulus onset: 150–250 ms, 400–550 ms, and 700–800 ms. Exploratory localization of the EEG signal revealed a shifting network of activity moving from sensory and memory structures to areas associated with value coding, with stimulus value activity localized to vmPFC only from 400 ms onwards. Consistent with these results, functional connectivity analyses also showed a causal flow of information from temporal cortex to vmPFC. Thus, although value signals are present as early as 150 ms after stimulus onset, the value signals in vmPFC appear relatively late in the choice process, and seem to reflect the integration of incoming information from sensory and memory related regions
On the Application of Data Mining to Official Data
Retrieving valuable knowledge and statistical patterns from official data has a great potential in supporting strategic policy making. Data Mining (DM) techniques are well-known for providing flexible and efficient analytical tools for data processing. In this paper, we provide an introduction to applications of DM to official statistics and flag the important issues and challenges. Considering recent advancements in software projects for DM, we propose intelligent data control system design and specifications as an example of DM application in official data processing.Data mining, Official data, Intelligent data control system
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