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Density-Based Clustering of Polygons

by Deepti Joshi, Ashok K. Samal, Leen-kiat Soh
"... Abstract – Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. P-DBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate their top ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
Abstract – Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. P-DBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate

Density-Based Clustering Validation

by Jörg Sander
"... One of the most challenging aspects of clustering is valida-tion, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular cl ..."
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clusters. Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly contain-ing noise objects. In these cases relative validity indices pro-posed for globular cluster validation may fail. In this paper

Stability of Density-Based Clustering

by Alessandro Rinaldo, Aarti Singh, Rebecca Nugent, Sanjoy Dasgupta
"... 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) ..."
Abstract - Cited by 17 (6 self) - Add to MetaCart
(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

Efficient Anytime Density-based Clustering

by Son T. Mai, Xiao He, Jing Feng, Christian Böhm
"... Many clustering algorithms suffer from scalability problems on massive datasets and do not support any user interaction during runtime. To tackle these problems, anytime clustering algorithms are proposed. They produce a fast approximate result which is continuously refined during the further run. A ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
. Also, they can be stopped or suspended anytime and provide an answer. In this paper, we propose a novel anytime clustering algorithm based on the density-based clustering paradigm. Our algorithm called A-DBSCAN is applicable to very high dimensional databases such as time series, trajectory, medical

Multi-Step Density-Based Clustering

by Stefan Brecheisen, Hans-peter Kriegel, Martin Pfeifle
"... Abstract. Data mining in large databases of complex objects from scientific, engineering or multimedia applications is getting more and more important. In many areas, complex distance measures are first choice but also simpler distance functions are available which can be computed much more efficien ..."
Abstract - Cited by 25 (9 self) - Add to MetaCart
efficiently. In this paper, we will demonstrate how the paradigm of multi-step query processing which relies on exact as well as on lower-bounding approximated distance functions can be integrated into the two density-based clustering algorithms DBSCAN and OPTICS resulting in a considerable efficiency boost

Locally Scaled Density Based Clustering

by Ergun Bic, Deniz Yuret
"... 1 Introduction Clustering is the process of allocating points in a given dataset into disjoint and mean-ingful 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 density-based 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

Hierarchical Density-Based Clustering of Shapes

by Temujin Gautama, Marc M. Van Hulle - IEEE Neural Network for Signal Processing Workshop 2001, Massachusetts, in press , 2001
"... This article describes a novel way of representing large databases of shapes. We propose a hierarchical clustering of a set of Fouriertransformed contours. The clustering analysis is density-based and is performed using topographic maps. We have tested the approach on a database of extracted contour ..."
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This article describes a novel way of representing large databases of shapes. We propose a hierarchical clustering of a set of Fouriertransformed contours. The clustering analysis is density-based and is performed using topographic maps. We have tested the approach on a database of extracted

Locally Scaled Density Based Clustering

by Ergun Biçici, Deniz Yuret - In ICANNGA 2007, LNCS , 2007
"... Abstract. Density based clustering methods allow the identification of arbitrary, not necessarily convex regions of data points that are densely populated. The number of clusters does not need to be specified beforehand; a cluster is defined to be a connected region that exceeds a given density thre ..."
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Abstract. Density based clustering methods allow the identification of arbitrary, not necessarily convex regions of data points that are densely populated. The number of clusters does not need to be specified beforehand; a cluster is defined to be a connected region that exceeds a given density

for Density based Clustering of Web

by Ms K. Santhisree, Dr. A Damodaram
"... Incorporating a new similarity measure ..."
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Incorporating a new similarity measure

Fully Adaptive Density-Based Clustering

by Ingo Steinwart
"... ar ..."
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