This article proposes a semi-interactive system for visual data exploration using an iterative clustering that combines an automatic approach with an interactive one. We propose a framework to improve the interactivity between the user and the data analysis process, allowing him or her to participate actively in the iterative clustering tasks using a two-dimensional projection. Defining a cluster by its seed (center) and its limit, the proposed approach allows the user to modify the automated values or to define new seeds and the associated cluster limit himself or herself. The user can perform the clustering according to his or her visual perception manually and can also choose to let the automated approach find optimal seeds and then interact with the process to iterate the clustering process according to his or her visual perception and domain knowledge. Most of the evaluation criteria for clustering evaluate the complete clustering and not each cluster separately. In this article, we propose to adapt evaluation criteria to single clusters, allowing the users to evaluate their own clusters and perform the clustering iteratively until satisfaction. To evaluate our proposed approach, we conduct a user evaluation, where the users are asked to perform clustering interactively according to their visual perception and with the semi-interactive one. We also compare the obtained results with those of automated clustering. The quantitative results have shown that the cooperative approach can improve the clustering results in terms of accuracy.

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<p>We present an implementation of a time series analysis toolbox for remote sensing imagery in R which has been largely funded by the European Space Agency within the PROBA-V MEP Third Party Services project. The toolbox is developed according to the needs of the time series analysis community. The data is provided by the PROBA-V mission exploitation platform (MEP) at VITO. The toolbox largely builds on existing specialized R packages and functions for raster and time series analysis combining these in a common framework.</p><p>In order to ease access and usage of the toolbox, it has been deployed in the MEP Spark Cluster to bring the algorithm to the data. All functions are also wrapped in a Web Processing Service (WPS) using 52°North’s WPS4R extension for interoperability across web platforms. The WPS can be orchestrated in the Automatic Service Builder (ASB) developed by Space Applications. Hence, the space-time analytics developed in R can be integrated into a larger workflow potentially integrating external data and services. The WPS provides a Webclient including a preview of the results in a map window for usage within the MEP. Results are offered for download or through Web Mapping and Web Coverage Services (WMS, WCS) provided through a Geoserver instance.</p><p>Through its interoperability features the EOTSA toolbox provides a contribution towards collaborative science.</p>

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