Details
| Original language | English |
|---|---|
| Pages (from-to) | 75-82 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 33 |
| Publication status | Published - 2000 |
| Externally published | Yes |
| Event | 19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000 - Amsterdam, Netherlands Duration: 16 Jul 2000 → 23 Jul 2000 |
Abstract
The automatic analysis of spatial data sets presumes to have techniques for interpretation and structure recognition. Such procedures are especially needed in GIS and digital cartography in order to automate the time-consuming data update and to generate multi-scale representations of the data. In order to infer higher level information from a more detailed data set, coherent, homogeneous structures in a data set have to be delineated. There are different approaches to tackle this problem, e.g. model based interpretation, rule based aggregation or clustering procedures. In the paper, a parameter-free graph-based clustering approach and an application in the domain of cartography, namely typification is presented. Typification is a generalization operation needed in order to present a set of objects by a subset of representatives. In this way, a collection of objects can be represented by fewer objects in a symbolic representation. An important prerequisite for the legibility of detailed representation is that the structure is preserved. This implies that object clusters are preserved.
Keywords
- Clustering, Data aggregation, Digital cartography, GIS, Spatial data interpretation, Typification
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 33, 2000, p. 75-82.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Parameter-free cluster detection in spatial databases and its application to typification
AU - Anders, Karl Heinrich
AU - Sester, Monika
PY - 2000
Y1 - 2000
N2 - The automatic analysis of spatial data sets presumes to have techniques for interpretation and structure recognition. Such procedures are especially needed in GIS and digital cartography in order to automate the time-consuming data update and to generate multi-scale representations of the data. In order to infer higher level information from a more detailed data set, coherent, homogeneous structures in a data set have to be delineated. There are different approaches to tackle this problem, e.g. model based interpretation, rule based aggregation or clustering procedures. In the paper, a parameter-free graph-based clustering approach and an application in the domain of cartography, namely typification is presented. Typification is a generalization operation needed in order to present a set of objects by a subset of representatives. In this way, a collection of objects can be represented by fewer objects in a symbolic representation. An important prerequisite for the legibility of detailed representation is that the structure is preserved. This implies that object clusters are preserved.
AB - The automatic analysis of spatial data sets presumes to have techniques for interpretation and structure recognition. Such procedures are especially needed in GIS and digital cartography in order to automate the time-consuming data update and to generate multi-scale representations of the data. In order to infer higher level information from a more detailed data set, coherent, homogeneous structures in a data set have to be delineated. There are different approaches to tackle this problem, e.g. model based interpretation, rule based aggregation or clustering procedures. In the paper, a parameter-free graph-based clustering approach and an application in the domain of cartography, namely typification is presented. Typification is a generalization operation needed in order to present a set of objects by a subset of representatives. In this way, a collection of objects can be represented by fewer objects in a symbolic representation. An important prerequisite for the legibility of detailed representation is that the structure is preserved. This implies that object clusters are preserved.
KW - Clustering
KW - Data aggregation
KW - Digital cartography
KW - GIS
KW - Spatial data interpretation
KW - Typification
UR - http://www.scopus.com/inward/record.url?scp=84993964830&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84993964830
VL - 33
SP - 75
EP - 82
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
T2 - 19th International Congress for Photogrammetry and Remote Sensing, ISPRS 2000
Y2 - 16 July 2000 through 23 July 2000
ER -