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DBScan,基于高密度联通区域的基于密度的聚类方法
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DBSCAN
A Density-Based Algorithm for
Discovering Clusters
in Large Spatial Databases with
Noise
Francesco Santini
Phd Student
Spatial Data
Numerous applications require the
management of data related to space.
Spatial Database Systems: satellite images,
geological data, X-ray cristallography.
Class identification: the grouping of the
objects of a database into meaningful
subclasses.
In an earth observation database, we might
want to discover classes of houses along
some river.
Requirements in Large Spatial
Database
1.
Minimal requirements of domain
knowledge to determine the input
parameters.
2.
Discovery of clusters with arbitrary shape
(e.g. spherical, linear, elongated).
3.
Good efficiency.
Clustering Methods
Clustering Methods
Hierarchical
DIANA
Agglomerative
AGNES
Divisive
CUREBIRCH
Chameleon
Density
DBSCAN
OPTICS
DENCLUE
Partitioning
CLARANS
k-Medoids
STING
Clustering Algorithms
Partitioning Alg: Construct various partitions
then evaluate them by some criterion
(CLARANS, k-medoid, O(n) calls).
Hierarchy Alg: Create a hierarchical
decomposition of the set of data (or objects)
using some criterion (merge & divisive,
difficult to find termination condition).
Density-based Alg: based on local
connectivity and density functions.
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