Beyond Querying - User-Centered Data Management - page 46

Figure 3.21: The visual interface of KnowledgeSTUDIO.
the exploitation of other visualization toolkits. The infrastructure is more of a visual exploration
environment than a core data mining system.
TempleMVV ( Mihalisin and Timlin , 1995 ) may be traced back to MVV ( Mihalisin et al. ,
1991 ). The latter uses bar charts (histogram within histogram within histogram) and slide bars (with
horizontal scales) to locate clusters in multidimensional space that allows the display of multiple views
of a given dataset.TempleMVV is a tool that has been proposed for fast visual data mining. It provides
hierarchical visualizations for any mix of categorical and continuous attributes. Its visualization
paradigm is based on nested attributes, with four attributes being represented at the same time.
VidaMine is a visual data mining system that aims at providing the user with a consistent,
uniform, flexible and intuitive visual interaction environment in order to allow or enable the user
not only to process data but also to steer, guide or direct the entire process of mining knowl-
edge ( Kimani et al. , 2008 ). Its visual interface offers visual interaction environments across different
mining techniques and tasks. At present, the system offers visual environments for mining meta-
queries, performing clustering, and mining association rules. Each visual environment comprises six
visual parts/sections. Each visual part corresponds to some mining subtask such as the construction
of target dataset, the selection of a data mining algorithm, the visualization of mining results, etc.