A density-based spatial clustering for physical constraints

A density-based spatial clustering for physical constraints

We propose a spatial clustering method, called DBRS+, which aims to cluster spatial data in the presence of both obstacles and facilitators. It can handle datasets with intersected obstacles and facilitators. Without preprocessing, DBRS+ processes constraints during clustering. It can find clusters...

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Veröffentlicht in:Journal of intelligent information systems 2012-02, Vol.38 (1), p.269-297
Hauptverfasser: Wang, Xin, Rostoker, Camilo, Hamilton, Howard J.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Artificial Intelligence
Clustering
Clusters
Computer Science
Crime
Data mining
Data Structures and Information Theory
Datasets
Handles
Information Storage and Retrieval
Information systems
IT in Business
Materials handling
Methods
Natural Language Processing (NLP)
Neighborhoods
Obstacles
Preprocessing
Spatial data
Studies
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Beschreibung
Zusammenfassung:We propose a spatial clustering method, called DBRS+, which aims to cluster spatial data in the presence of both obstacles and facilitators. It can handle datasets with intersected obstacles and facilitators. Without preprocessing, DBRS+ processes constraints during clustering. It can find clusters with arbitrary shapes. DBRS+ has been empirically evaluated using synthetic and real data sets and its performance has been compared to DBRS and three related methods for handling obstacles, namely AUTOCLUST+, DBCLuC*, and DBRS_O.
ISSN:0925-9902
1573-7675
DOI:10.1007/s10844-011-0154-7