Data mining of maps and their automatic region-time-theme classification-Reference-Cited by-同舟云学术

Data mining of maps and their automatic region-time-theme classification

Published:2009-03 Issue:1 Volume:1 Page:39-44
ISSN:1946-7729
Container-title:SIGSPATIAL Special
language:en
Short-container-title:SIGSPATIAL Special

Author:

Gelernter Judith¹

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA

Abstract

The goal of this research is to organize maps mined from journal articles into categories for hierarchical browsing within region, time and theme facets. A 150-map training set collected manually was used to develop classifiers. Metadata pertinent to the maps were harvested and then run separately though knowledge sources and our classifiers for region, time and theme. Evaluation of the system based on a 54-map test set of unseen maps showed 69%--93% classification accuracy when compared with two human classifications for the same maps. Data mining and semantic analysis methods used here could support systems that index other types of article components such as diagrams or charts by region, time and theme.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/1517463.1517468

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