Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories

Rafal. A Angryk

ISBN 10: 3319998722 / ISBN 13: 9783319998725

Edité par Springer International Publishing, Springer International Publishing, 2018

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - This SpringerBrief provides an overview within data mining of spatiotemporal frequentpattern mining from evolving regions to the perspective of relationshipmodeling among the spatiotemporal objects, frequent pattern miningalgorithms, and data access methodologies for mining algorithms. Whilethe focus of this book is to provide readers insight into the miningalgorithms from evolving regions, the authors also discuss datamanagement for spatiotemporal trajectories, which has becomeincreasingly important with the increasing volume of trajectories.This brief describes state-of-the-art knowledge discoverytechniques to computer science graduatestudents who are interested in spatiotemporal data mining, as well asresearchers/professionals, who deal with advanced spatiotemporal dataanalysis in their fields. These fields include GIS-experts, meteorologists,epidemiologists, neurologists, and solar physicists.

N° de réf. du vendeur 9783319998725

Détails bibliographiques

• Titre: Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
• Éditeur: Springer International Publishing, Springer International Publishing
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 3319998722
• ISBN à 13 chiffres: 9783319998725
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 7 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 7 millimètres de profondeur
• Poids: 195 grammes

A propos de ce titre

Synopsis

This SpringerBrief provides an overview within data mining of spatiotemporal frequent pattern mining from evolving regions to the perspective of relationship modeling among the spatiotemporal objects, frequent pattern mining algorithms, and data access methodologies for mining algorithms. While the focus of this book is to provide readers insight into the mining algorithms from evolving regions, the authors also discuss data management for spatiotemporal trajectories, which has become increasingly important with the increasing volume of trajectories.

This brief describes state-of-the-art knowledge discovery techniques to computer science graduate students who are interested in spatiotemporal data mining, as well as researchers/professionals, who deal with advanced spatiotemporal data analysis in their fields. These fields include GIS-experts, meteorologists, epidemiologists, neurologists, and solar physicists.