EnsembleGraph: Interactive visual analysis of spatiotemporal behaviors in ensemble simulation data

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

“…Line plots, heatmaps, and dendrograms are the most widely adopted visualization techniques with hierarchical clustering e.g. [34], [37], [39], [46], [50], [52], [60], [85], [89], [91], [92].…”

“…Aghabozorgi et al [12] state that density-based clustering has not been used broadly for time series data in the data mining community as it has some complexity. However, we found that many of our surveyed visual analytics papers have adopted density-based methods [34], [42], [48], [57], [64], [66], [67], [69], [71], [73], [74], [77], [78], [89]. Looking at combinations of visualization with clustering algorithms, the surveyed papers indicate that the trend is dominated by trajectory data that often adopts density-based techniques for clustering compared to other clustering algorithms.…”

“…k-Means has been used to cluster time series data, achieving efficient clustering results due to its speed, simplicity, ease of implementation, and the possibility to assign the desired amount of clusters [43], [136]. Most of the surveyed papers use commonly applied partitioning methods of clustering, especially the k-Means algorithm [34], [36], [38], [43], [52], [58], [74], [75], [77], [78], [84], [86], [87], [89], [90], [95]. k-Means clustering can be performed on multivariate time series, where each time point is considered as a vector and the cluster labels are used as symbols to encode the time series [43].…”

“…Typically, the x-axis represents time, with multiple axes radiating from the centre indicating durations (e.g., hours in a day, days in a month, or months in a year). Transformed data may place spatial coordinates on the x-axis (with a map central to the visualisation), and the y-dimension could be time, with distance from the y-dimension then indicating further attributes such as intensity (of the sensor -e.g., pollution levels [86], [89], and shells of data appear around the circle (stacked/river charts). Multiple small versions would create glyphs, or a single view linked to other views, which offers more detail.…”

“…By exposing algorithm choice through the interface, along with parameters, the user can play an interactive role in deciding the best approach for their data [44], as effective algorithms for time series analysis always require precise choices of approaches and parameters in order to be able to solve clustering and classification tasks. We notice from our survey that several interaction methods are not specific to data only, interestingly, a variety of interactive tools are combined to support analysts in algorithm selection (e.g., [89], [113]), training (e.g., [44], [113]) and testing (e.g. [113]).…”