| Abstrakt: |
This chapter describes a new technique for extracting patterns and relations visually from multidimensional binary data using monotone Boolean functions. Visual Data Mining has shown benefits in many areas when used with numerical data, but that technique is less beneficial for binary data. This problem is especially challenging in medical applications tracked with binary symptoms. The proposed method relies on monotone structural relations between Boolean vectors in the n-dimensional binary cube, En, and visualizes them in 2-D as chains of Boolean vectors. Actual Boolean vectors are laid out on this chain structure. Currently the system supports two visual forms: the multiple disk form (MDF) and the "Yin/Yang" form (YYF). In the MDF, every vector has a fixed horizontal and vertical position. In the YYF, only the vertical position is fixed. Key words: Visual Data Mining, explicit data structure, Boolean data, Monotone Boolean Function, Hansel Chains, Binary Hypercube. [ABSTRACT FROM AUTHOR] |
