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467
A densitybased algorithm for discovering clusters in large spatial databases with noise
, 1996
"... Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clu ..."
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Cited by 1786 (70 self)
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of clusters with arbitrary shape and good efficiency on large databases. The wellknown clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a densitybased notion of clusters which is designed to discover
DensityBased Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications
"... The clustering algorithm DBSCAN relies on a densitybased notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we generalize this algorithm in two important directions. The generalized algorithm called GDBSCAN can cluster poin ..."
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Cited by 189 (9 self)
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The clustering algorithm DBSCAN relies on a densitybased notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we generalize this algorithm in two important directions. The generalized algorithm called GDBSCAN can cluster
A DENSITYBASED MICRO AGGREGATION TECHNIQUE FOR PRIVACYPRESERVING DATA MINING
"... Microaggregation is an effective means of protecting the microdata in the statistical databases. Microaggragation protects the microdata by partitioning them into groups of at least k records in each group and substituting the records in each group with the centroid of the group. An optimal microagg ..."
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microaggregation can be achieved by minimizing the information loss incurred from the aggregation. This paper presents a densitybased microagregation method for protecting the numeric data employing the densitybased notion of clustering. In this work we provide a microaggragation method which reduces the risk
DGLC: a DensityBased Global Logical Combinatorial Clustering
 Intergovernmental Panel on Climate Change (IPCC
, 2000
"... Clustering has been widely used in areas as Pattern Recognition, Data Analysis and Image Processing. Recently, clustering algorithms have been recognized as one of a powerful tool for Data Mining. However, the wellknown clustering algorithms offer no solution to the case of Large Mixed Incomplete D ..."
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mixed incomplete data sets. This algorithm combines the ideas of Logical Combinatorial Pattern Recognition with the Density Based Notion of Cluster . Finally, an example is showed in order to illustrate the work of the algorithm.
Stability of DensityBased Clustering
"... High density clusters can be characterized by the connected components of a level set L(λ) = {x: p(x)> λ} of the underlying probability density function p generating the data, at some appropriate level λ ≥ 0. The complete hierarchical clustering can be characterized by a cluster tree T = ⋃ λ L(λ) ..."
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Cited by 16 (4 self)
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(λ). In this paper, we study the behavior of a density level set estimate L(λ) and cluster tree estimate T based on a kernel density estimator with kernel bandwidth h. We define two notions of instability to measure the variability of L(λ) and T as a function of h, and investigate the theoretical properties
ASCENT: Adaptive selfconfiguring sensor networks topologies
, 2004
"... Advances in microsensor and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. The low pernode cost will allow these wireless networks of sensors and actuators to be densely distributed. The nodes in these dense networks w ..."
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Cited by 449 (15 self)
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configuration, and the environmental dynamics will preclude designtime preconfiguration. Therefore, nodes will have to selfconfigure to establish a topology that provides communication under stringent energy constraints. ASCENT builds on the notion that, as density increases, only a subset of the nodes
DGLC: a DensityBased Global Logical Combinatorial Clustering Algorithm for Large Mixed Incomplete Data
 Intergovernmental Panel on Climate Change (IPCC
, 2002
"... Clustering has been widely used in areas as Pattern Recognition, Data Analysis and Image Processing. Recently, clustering algorithms have been recognized as one of a powerful tool for Data Mining. However, the wellknown clustering algorithms offer no solution to the case of Large Mixed Incomplete D ..."
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Cited by 2 (0 self)
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mixed incomplete data sets. This algorithm combines the ideas of Logical Combinatorial Pattern Recognition with the Density Based Notion of Cluster . Finally, an example is showed in order to illustrate the work of the algorithm. 1.
A New VoronoiBased Surface Reconstruction Algorithm
, 2002
"... We describe our experience with a new algorithm for the reconstruction of surfaces from unorganized sample points in R³. The algorithm is the first for this problem with provable guarantees. Given a “good sample” from a smooth surface, the output is guaranteed to be topologically correct and converg ..."
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Cited by 414 (9 self)
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and convergent to the original surface as the sampling density increases. The definition of a good sample is itself interesting: the required sampling density varies locally, rigorously capturing the intuitive notion that featureless areas can be reconstructed from fewer samples. The output mesh interpolates
Locally Scaled Density Based Clustering
"... 1 Introduction Clustering is the process of allocating points in a given dataset into disjoint and meaningful clusters. Density based clustering methods allow the identification of arbitrary, not necessarily convex regions of data points that are densely populated. Density basedclustering does not ..."
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not need the number of clusters beforehand but relies on a densitybased notion of clusters such that for each point of a cluster the neighborhood of a given radius( ") has to contain at least a minimum number of points (""). However, finding the correctparameters for standard
A Fast Parallel Clustering Algorithm for Large Spatial Databases
 DATA MINING AND KNOWLEDGE DISCOVERY, 3, 263–290
, 1999
"... The clustering algorithm DBSCAN relies on a densitybased notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we present PDBSCAN, a parallel version of this algorithm. We use the ‘sharednothing’ architecture with multiple compu ..."
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Cited by 54 (1 self)
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The clustering algorithm DBSCAN relies on a densitybased notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we present PDBSCAN, a parallel version of this algorithm. We use the ‘sharednothing’ architecture with multiple
Results 1  10
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467