Visualization of Mixed Attributed High-Dimensional Dataset Using Singular Value Decomposition

Autor:	K. Poulose Jacob, Bindiya M. Varghese, A. Unnikrishnan
Rok vydání:	2017
Předmět:	Data set Singular value Data visualization business.industry Computer science Dimensionality reduction Singular value decomposition Pattern recognition Point (geometry) Artificial intelligence business Categorical variable Visualization
Zdroj:	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783319589664 BDTA
DOI:	10.1007/978-3-319-58967-1_1
Popis:	The ability to present data or information in a pictorial format makes data visualization, one of the major requirement in all data mining efforts. A thorough study of techniques, which presents visualization, it was observed that many of the described techniques are dependent on data and the visualization needs support specific to domain. On contrary, the methods based on Eigen decomposition, for elements in a higher dimensional space give meaningful depiction. The illustration of the mixed attribute data and categorical data finally signifies the data set a point in higher dimensional space, the methods of singular value decomposition were applied for demonstration in reduced dimensions (2 and 3). The data set is then projected to lower dimensions, using the prominent singular values. The proposed methods are tested with datasets from UCI Repository and compared.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2d7b0c7caafae0f84a02531831720e6b https://doi.org/10.1007/978-3-319-58967-1_1 Zobrazit plný text záznamu