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Large amounts of data are ubiquitous today. Data mining methods like clustering were introduced to gain knowledge from these data. Recently, detection of multiple clusterings has become an active research area, where several alternative clustering solutions are generated for a single dataset. Each of the obtained clustering solutions is valid, of importance, and provides a different interpretation of the data. The key for knowledge extraction, however, is to learn how the different solutions are related to each other. This can be achieved by a comparison and analysis of the obtained clustering solutions. We introduce our demo MCExplorer, the first tool that allows for interactive exploration, browsing, and visualization of multiple clustering solutions on several granularities. MCExplorer is applicable to the output of both fullspace and subspace clustering approaches.