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In this paper, we used X-Means clustering algorithm, incorporated data images from a so-called Iterative Data Image Rotated Bar Graph (iDIRBrG) method (formerly referred as BC method) and used Vector Fusion Visualization to achieve better traffic data analysis results compared to our previous work by improving how data signatures are constructed from the raw data set. By doing so, we effectively identify more patterns, extracted known as well as unexpected outliers, and novel information from the data set. These results were validated by experts from the NCTS. Furthermore, we also show more simplistic frequency-domain 2D visualizations of the entire data set. These successfully expose the internal structure and relationship of each individual point as well as the clusters obtained from it. Hence, using the methods above, we provide an effective and efficient time series traffic analysis.

Building automation systems record operation data including physical values, system states and operation conditions. This data is stored, but commonly not automatically evaluated. This historic data is the key to efficient operation and to quick recognition of errors and inefficiencies, a potential that is not exploited today. Instead, today the evaluation during operation delivers only alarming in case of system failures. Analysis is commonly done by the facility manager, who uses his experience to interpret data. Methods from data mining and data analysis can contribute to a better understanding of building operation and provide the necessary information to optimize operation, especially in the area of Heating, Ventilation and Air Conditioning (HVAC) systems. Increases in energy efficiency and can be achieved by automated data analysis and by presenting the user energy performance indicators of all relevant HVAC components. The authors take a first step to examine operation data of adsorption chillers using the X-Means algorithm to automate the detection of system states.

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