Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Crust and anti-crust: a one-step boundary and skeleton extraction algorithm
SCG '99 Proceedings of the fifteenth annual symposium on Computational geometry
ACM Computing Surveys (CSUR)
Fuzzy objects for geographical information systems
Fuzzy Sets and Systems - Special issue on Uncertainty in geographic information systems and spatial data
Why so many clustering algorithms: a position paper
ACM SIGKDD Explorations Newsletter
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Robustness of density-based clustering methods with various neighborhood relations
Fuzzy Sets and Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Assigning footprints to dot sets: an analytical survey
COSIT'09 Proceedings of the 9th international conference on Spatial information theory
What is the region occupied by a set of points?
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
On the shape of a set of points in the plane
IEEE Transactions on Information Theory
Fuzzy and crisp clustering methods based on the neighborhood concept: A comprehensive review
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS'2011: 2nd International Fuzzy Systems Symposium
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Geometric footprints, which delineate the region occupied by a spatial point pattern, serve a variety of functions in GIScience. This research explores the use of two density-based clustering algorithms for footprint generation. First, the Density-Based Spatial Clustering with Noise (DBSCAN) algorithm is used to classify points as core points, non-core points, or statistical noise; then a footprint is created from the core and non-core points in each cluster using convex hulls. Second, a Fuzzy-Neighborhood (FN)-DBSCAN algorithm, which incorporates fuzzy set theory, is used to assign points to clusters based on membership values. Then, two methods are presented for delineating footprints with FN-DBSCAN: (1) hull-based techniques and (2) contouring methods based on interpolated membership values. The latter approach offers increased flexibility for footprint generation, as it provides a continuous surface of membership values from which precise contours can be delineated. Then, a heuristic parameter selection method is described for FN-DBSCAN, and the approach is demonstrated in the context of wildlife home range estimation, where the goal is to a generate footprint of an animal's movements from tracking data. Additionally, FN-DBSCAN is applied to produce crime footprints for a county in Florida. The results are used to guide a discussion of the relative merits of the new techniques. In summary, the fuzzy clustering approach offers a novel method of footprint generation that can be applied to characterize a variety of point patterns in GIScience.