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National Repository of Grey Literature 2 records found  Search took 0.02 seconds. 
Visualization and testing of team profiles
 Höschl, Cyril
The aim of this thesis is to demonstrate methods for visualization and testing team profiles. An individual's profile is a vector of scores representing personality of a team member. Team profile is a collection of individual profiles. The first step for visualization of team profile is to find similarities among individual profiles. We present new approach for measuring similarity between two profiles as a significance of difference expressed by probability. We use this measure both for testing mean difference between two subgroups of profiles and also for building similarity matrix. We represent team profiles as a 3D ladscape map called Sociomap. To create a sociomap we demonstrate own algorithm for projecting similarity matrix into a 2D plane. We compare this algorithm with well known Multidimensional scaling and Principal Component Analysis methods solving the same task. Comparing with MDS and PCA, our algorithm provides more accurate result with comparable demand of time. We also employ some well known Computer Graphics methods to improve visual 3D representation of the sociomap and we also show some new improvements to render sociomap more realistic.
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Visualization and testing of team profiles
 Höschl, Cyril
The aim of this thesis is to demonstrate methods for visualization and testing team profiles. An individual's profile is a vector of scores representing personality of a team member. Team profile is a collection of individual profiles. The first step for visualization of team profile is to find similarities among individual profiles. We present new approach for measuring similarity between two profiles as a significance of difference expressed by probability. We use this measure both for testing mean difference between two subgroups of profiles and also for building similarity matrix. We represent team profiles as a 3D ladscape map called Sociomap. To create a sociomap we demonstrate own algorithm for projecting similarity matrix into a 2D plane. We compare this algorithm with well known Multidimensional scaling and Principal Component Analysis methods solving the same task. Comparing with MDS and PCA, our algorithm provides more accurate result with comparable demand of time. We also employ some well known Computer Graphics methods to improve visual 3D representation of the sociomap and we also show some new improvements to render sociomap more realistic.
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