Visual Interactive Neighborhood Mining on High Dimensional Data
نویسندگان
چکیده
Cluster analysis is widely used for explorative data analysis, however, it is not trivial to select the right method and optimal parameters. Moreover, not all clustering methods can work with raw or dirty data. In this paper, we introduce an interactive data exploration tool, VINeM, which combines interactive mining with unsupervised tools by exploiting an intuitive neighborhood-based visualization technique. Local neighborhood based visualization is useful not only for analyzing multiple (dis-)similarity measures but also for effectively discarding noise. VINeM works well with high dimensional data and can be used to find subspace clusters.
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