Symbolic data analyses is an interdisciplinary study area that performs on a set of procedures to disassemble more intricate information sets. Instead of the standard statistical ways, the lack of flexibility and usefulness makes it much harder to comprehend a handful of size-data. To extract values from that enormous data, distinctive analysis strategies have been devised, thus the book “Symbolic Data Analyses and the Superior SODA software”, written by Edwin.
The fact that it can provide you all of the conceivable features of complex information and associated techniques is different compared to traditional text-mining means. Information and ideas regarding the outcomes are made available to the public. In addition, it allows the right person to forecast future developments. This was the objective of a multinational team headed by Dr. Edwin DiDAY as an investigator to set up research under the scope of a three-year project sponsored by EUROSTAR.
Электронная Книга «Symbolic Data Analysis and the SODAS Software» написана автором Edwin Diday в году.
Минимальный возраст читателя: 0
Язык: Английский
ISBN: 9780470723555
Описание книги от Edwin Diday
Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such data. Symbolic data methods differ from that of data mining, for example, because rather than identifying points of interest in the data, symbolic data methods allow the user to build models of the data and make predictions about future events. This book is the result of the work f a pan-European project team led by Edwin Diday following 3 years work sponsored by EUROSTAT. It includes a full explanation of the new SODAS software developed as a result of this project. The software and methods described highlight the crossover between statistics and computer science, with a particular emphasis on data mining.
