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http://dspace.cityu.edu.hk/handle/2031/9410
Title: | Algorithms for data visualization |
Authors: | Lau, Wing Ho (劉榮浩) |
Department: | Department of Computer Science |
Issue Date: | 2020 |
Course: | CS4514 Project |
Programme: | Bachelor of Science (Honours) in Computer Science |
Supervisor: | Dr. Li, Shuaicheng |
Citation: | Lau, W. H. (2020). Algorithms for data visualization (Outstanding Academic Papers by Students (OAPS), City University of Hong Kong). |
Abstract: | In visualization, we frequently utilize the terms information. In numerous cases, they represent to distinctive levels of deliberation, understanding or realness. Data visualization could be a exceptionally modern and promising zone of computer science. It employments computer illustrations to uncover designs, trends, and connections in a information set. In this article, we to begin with familiarize with data visualization and its related concepts, and after that we'll think about a few common algorithms for data visualization. This paper focus on tree visualisation, a novel visualization technique for representing hierarchical data sets. The recursive branching structure of the tree is very similar to the way people organize information. The tree diagram can be represented in a persuasive and easy-to-understand visualization. With a glance, people can know what is going on. The decision tree is actually a kind of tree diagram. I believe everyone is familiar with it. A decision tree could be a choice bolster apparatus that employs a tree choice chart for show and it's conceivable consequences, including crises, asset costs, and utility. In addition, tree diagrams are also important for bioinformatics. For example, treemap is a very famous presentation method in data visualization technology. During analysis, it provides a variety of treemap appearances based on species annotation results and relative abundance information of groups. Take the genus and three groups as examples. The block size represents the relative abundance in the group. Colors are used to distinguish the door class to which the species belongs, and the species of the same door class are distinguished by color depth. Intuitive discrimination. Each block also displays the complete species name and relative abundance for easy reading. The advantages of tree diagram analysis can visually distinguish the differences in the composition of bacteria between different groups, and the relationship between the relative abundance ratio of each species in the group and the number of species belonging to the phyla class. |
Appears in Collections: | OAPS - Dept. of Computer Science |
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