Tuesday, January 20, 2015

Boxplots grouped by categories

Simulating self-organizing systems often requires a compact representation of numerical data. One way to achieve this is via Boxplots,which indicate statisical distributions of data series through their quartiles. Usually, a box shows the median, the lower and the upper quartile values of a data series. The whiskers depict the lowest datum still withing 1.5 IQR (interquartile range) of the lower quartile and the highest datum still within 1.5 IQR of the upper quartile. Boxplots depict a good deal of information for statistical interpretation of data. Most of the tools for statistical computing and graphics can easily build boxplots, e.g., the boxplot function in R, the boxplot function in MATLAB, and the boxplot function in Python. As you can see, there are many affordable tools to display boxplots, but things get tricky if there is a need to group in categories. To achieve this, Sergii Zhevzhyk wrote a Python program using the matplotlib library which supports customization and adaptation of graphs. Data are loaded from the given csv files. One boxplot sample is shown below. The source code of our implementation can be found at GitHub.         
The image above shows the results of two measurements for different type of the candies. The comparison of two measurements can be done without problem, because they placed close to each other and have different colors. Two files (first file, second file) contain the data for this boxplot.


Call for Papers 10th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2015)

In conjunction with:
CISIS 2015

The 10th International Conference on Soft Computing Models in Industrial and Environmental Applications will take place in Burgos, Spain. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. This conference is mainly focus on its industrial and environmental applications.

Topics of interest include, but are not limited to:
• Green Computing
• Evolutionary Computing
• Neuro Computing
• Probabilistic Computing
• Immunological Computing
• Hybrid Methods
• Causal Models
• Case-based Reasoning
• Chaos Theory Fuzzy Computing
• Intelligent Agents and Agent Theory
• Interactive Computational Models

The application fields of interest cover, but are not limited to:
• Decision Support
• Process and System Control
• System Identification and Modelling
• Optimization
• Signal or Image Processing
• Vision or Pattern Recognition
• Condition Monitoring
• Fault Diagnosis
• Systems Integration
• Internet Tools
• Human Machine Interface
• Time Series Prediction
• Robotics
• Motion Control & Power Electronics
• Biomedical Engineering
• Virtual Reality
• Reactive Distributed AI
• Telecommunications
• Consumer Electronics
• Industrial Electronics
• Manufacturing Systems
• Power and Energy
• Data Mining
• Data Visualisation
• Intelligent Information Retrieval
• Bio-inspired Systems
• Autonomous Reasoning
• Intelligent Agents

Important Dates

Paper submission deadline: 24th January, 2015
Acceptance notification: 13th February, 2015
Submission of final papers: 3rd March, 2015
Final version submission: 13th March, 2015
Conference dates: 15th-17th June, 2015