Clustering is often a first step when trying to make sense of a large data set. A wide family of cluster analysis algorithms, namely hierarchical clustering algorithms, does not provide a partition of the data set but a hierarchy of clusters organized in a binary tree, known as a dendrogram. The dendrogram has a classical nodelink representation used by experts for various tasks like: to decide which subtrees are actual clusters (e.g., by cutting the dendrogram at a given depth); to give those clusters a name by inspecting their content; etc. We present Dendrogramix, a hybrid tree-matrix interactive visualization of dendrograms that superimposes the relationship between individual objects on to the hierarchy of clusters. Dendrogramix enables users to do tasks which involve both clusters and individual objects that are impracticable with the classical representation, like: to explain why a particular objects belongs to a particular cluster; to elicit and understand uncommon patterns (e.g., objects that could have been classified in a totally different cluster); etc. Those sensemaking tasks are supported by a consistent set of interaction techniques that facilitates the exploration of large clustering results.
People often use mobile devices to access information during conversations in casual settings, but mobile devices are not well suited for interaction in groups. Large situated displays promise to better support access to and sharing of information in casual conversations. This paper presents the LunchTable, a multi-user system based on semi-public displays that supports such casual group interactions around a lunch table. We describe our design goals and the resulting system, as well as a weeklong study of the interaction with the system in the lunch space of a research lab. Our results show substantial use of the LunchTable for sharing visual information such as online maps and videos that are otherwise difficult to share in conversations. Also, equal simultaneous access from several users does not seem critical in casual group interactions.
Hardware platforms of embedded systems are more powerful at each new generation thank to the integration of System-on-Chip (SoC). Developing streaming multimedia applications on embedded systems becomes an increasingly complex process. Modern applications are highly multi-threaded and have to decode the multimedia stream © ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Actes de la 28ème conférence francophone sur l'Interaction Homme-Machine, 2016.
There is an increasing need to quickly understand the contents log data. A wide range of patterns can be computed and provide valuable information: for example existence of repeated sequences of events or periodic behaviors. However pattern mining techniques often produce many patterns that have to be examined one by one, which is time consuming for experts. On the other hand, visualization techniques are easier to understand, but cannot provide the in-depth understanding provided by pattern mining approaches. Our contribution is to propose a novel visual analytics method that allows to immediately visualize hidden structures such as repeated sets/sequences and periodicity, allowing to quickly gain a deep understanding of the log.
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