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• W2098676125 abstract "Visualization methods could significantly improve the outcome of automated knowledge discovery systems by involving human judgment. Star coordinate is a visualization technique that maps k-dimensional data onto a circle using a set of axes sharing the same origin at the center of the circle. It provides the users with the ability to adjust this mapping, through scaling and rotating of the axes, until no mapped point-clouds (clusters) overlap one another. In this state, similar groups of data are easily detectable. However an effective adjustment could be a difficult or even an impossible task for the user in high dimensions. This is specially the case when the input space dimension is about 50 or more. In this paper, we propose a novel method toward automatic axes adjustment for high dimensional data in Star Coordinate visualization method. This method finds the best two-dimensional view point that minimizes intra-cluster distances while keeping the inter-cluster distances as large as possible by using label information. We call this view point a discernible visualization, where clusters are easily detectable by human eye. The label information could be provided by the user or could be the result of performing a conventional clustering method over the input data. The proposed approach optimizes the Star Coordinate representation by formulating the problem as a maximization of a Fisher discriminant. Therefore the problem has a unique global solution and polynomial time complexity. We also prove that manipulating the scaling factor alone is effective enough for creating any given visualization mapping. Moreover it is showed that k-dimensional data visualization can be modeled as an eigenvalue problem. Using this approach, an optimal axes adjustment in the Star Coordinate method for high dimensional data can be achieved without any user intervention. The experimental results demonstrate the effectiveness of the proposed approach in terms of accuracy and performance." @default.
• W2098676125 created "2016-06-24" @default.
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• W2098676125 date "2015-02-01" @default.
• W2098676125 modified "2023-09-23" @default.
• W2098676125 title "Discernible visualization of high dimensional data using label information" @default.
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• W2098676125 doi "https://doi.org/10.1016/j.asoc.2014.09.026" @default.
• W2098676125 hasPublicationYear "2015" @default.
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