9781599043876-1599043874-Temporal and Spatio-Temporal Data Mining

Temporal and Spatio-Temporal Data Mining

ISBN-13: 9781599043876
ISBN-10: 1599043874
Edition: 1
Author: Wynne Hsu, Mong Li Lee, Junmei Wang
Publication date: 2007
Publisher: IGI Global
Format: Hardcover 294 pages
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Book details

ISBN-13: 9781599043876
ISBN-10: 1599043874
Edition: 1
Author: Wynne Hsu, Mong Li Lee, Junmei Wang
Publication date: 2007
Publisher: IGI Global
Format: Hardcover 294 pages

Summary

Temporal and Spatio-Temporal Data Mining (ISBN-13: 9781599043876 and ISBN-10: 1599043874), written by authors Wynne Hsu, Mong Li Lee, Junmei Wang, was published by IGI Global in 2007. With an overall rating of 3.9 stars, it's a notable title among other Data Mining (Databases & Big Data) books. You can easily purchase or rent Temporal and Spatio-Temporal Data Mining (Hardcover) from BooksRun, along with many other new and used Data Mining books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.44.

Description

The recent surge of interest in spatio-temporal databases has resulted in numerous advances, such as: modeling, indexing, and querying of moving objects and spatio-temporal data. Aside from this, rule mining in spatial databases and temporal databases has been studied extensively in data mining research. Temporal and Spatio-Temporal Data Mining examines the problem of mining topological patterns in spatio-temporal databases by imposing the temporal constraints into the process of mining spatial collocation patterns. Temporal and Spatio-Temporal Data Mining presents probable solutions when discovering the spatial sequence patterns by incorporating the spatial information into the sequence of patterns, and introduces two new classes of spatial sequence patterns: flow patterns and generalized spatio-temporal patterns. This innovative book addresses different scenarios when finding complex relationships in spatio-temporal data by modeling them as graphs, giving readers a comprehensive synopsis on two successful partition-based algorithms designed by the authors.

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