Data visualization can be used to detect hidden structures and patterns in data sets that are found in data mining applications. However, although efficient data visualization algorithms to handle data sets with asymmetric proximities have been proposed, we develop an improved algorithm in this dissertation. In the first part of the proposal, we develop a modified Sammon mapping approach that uses the upper triangular part and the lower triangular part of an asymmetric distance matrix simultaneously. Our proposed approach is applied to two asymmetric data sets: an American college selection data set, and a Canadian college selection data set which contains rank information. When compared to other approaches that are used in practice…
Author: Li, Haiyan
Source: University of Maryland
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