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DensityBased Clustering
- in Spatial Databases: The Algortihm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery
, 1998
"... Abstract Most of the effort in the semi-supervised clustering literature was devoted to variations of the K-means algorithm. In this paper we show how background knowledge can be used to bias a partitional density-based clustering algorithm. Our work describes how labeled objects can be used to hel ..."
Abstract
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Cited by 33 (3 self)
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Abstract Most of the effort in the semi-supervised clustering literature was devoted to variations of the K-means algorithm. In this paper we show how background knowledge can be used to bias a partitional density-based clustering algorithm. Our work describes how labeled objects can be used
Density-Based Clustering of Polygons
"... 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 ..."
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Cited by 2 (1 self)
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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
"... 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
"... 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
Efficient Anytime Density-based Clustering
"... 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
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Cited by 2 (0 self)
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. 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
, 2006
"... 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 efficiently. In t ..."
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Cited by 25 (9 self)
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. 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. Our approach
Locally Scaled Density Based Clustering
"... 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
- 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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Cited by 1 (1 self)
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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
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