Lof

Breunig, Markus; Kriegel, Hans‐Peter; Ng, Raymond T.; Sander, Jöerg

doi:10.1145/342009.335388

Cited by 2,842 publications

(333 citation statements)

References 8 publications

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“…Nonparametric methods include, for instance, the well-known "distance-based" methods [Knorr and Ng 1997b;Ramaswamy et al 2000;Angiulli and Pizzuti 2002] and also "density-based" methods such as LOF (local outlier factor) [Breunig et al 2000] and its many variants. An important categorization of these approaches distinguishes "local" versus "global" approaches.…”

Section: Global and Local Outlier Detectionmentioning

confidence: 99%

Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

Campello

Moulavi

Zimek

et al. 2015

ACM Trans. Knowl. Discov. Data

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546

417

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An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan's classic model of density-contour clusters and trees. Such an algorithm generalizes and improves existing density-based clustering techniques with respect to different aspects. It provides as a result a complete clustering hierarchy composed of all possible density-based clusters following the nonparametric model adopted, for an infinite range of density thresholds. The resulting hierarchy can be easily processed so as to provide multiple ways for data visualization and exploration. It can also be further postprocessed so that: (i) a normalized score of "outlierness" can be assigned to each data object, which unifies both the global and local perspectives of outliers into a single definition; and (ii) a "flat" (i.e., nonhierarchical) clustering solution composed of clusters extracted from local cuts through the cluster tree (possibly corresponding to different density thresholds) can be obtained, either in an unsupervised or in a semisupervised way. In the unsupervised scenario, the algorithm corresponding to this postprocessing module provides a global, optimal solution to the formal problem of maximizing the overall stability of the extracted clusters. If partially labeled objects or instance-level constraints are provided by the user, the algorithm can solve the problem by considering both constraints violations/satisfactions and cluster stability criteria. An asymptotic complexity analysis, both in terms of running time and memory space, is described. Experiments are reported that involve a variety of synthetic and real datasets, including comparisons with state-of-the-art, density-based clustering and (global and local) outlier detection methods.

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Section: Global and Local Outlier Detectionmentioning

confidence: 99%

Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

Campello

Moulavi

Zimek

et al. 2015

ACM Trans. Knowl. Discov. Data

Self Cite

546

417

View full text Add to dashboard Cite

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“…We use the Local Outlier Factor (LOF) test [5]. It requires no knowledge of the underlying probability density function.…”

Section: Outlier Detection and Clusteringmentioning

confidence: 99%

“…Therefore, the infrastructural features cannot be used, as they would be sufficient on their own to discriminate between the individual building classes within the training samples. For the outlier detection, the number of buildings forming a cluster has been set to ten, as suggested in [5]. All houses with an LOF three times greater than the mean outlier factor are deleted from the dataset.…”

Section: Identifying Typical Buildingsmentioning

confidence: 99%

“…The contribution of this article is a new method for the enrichment of 3D city models by applying supervised (SVMs [35,36,43]; Decision Tree [1,25]) and unsupervised (X-Means [27]; LOF [5]) machine learning methods to coarse low resolution data, which are widely available. The method has been exemplary evaluated for the city of Bonn, but it can be extended to other types of cities as well, where other building types prevail.…”

Section: Introductionmentioning

confidence: 99%

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Automatic classification of building types in 3D city models

et al. 2011

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This article presents a classifier based on Support Vector Machines (SVMs), an advanced machine learning method for semantic enrichment of coarse 3D city models by deriving the building type. The information on the building type (detached building, terraced building, etc.) is essential for a variety of relevant applications of 3D city models like spatial marketing, real estate management and marketing, and for visualization. The derivation of the building type from coarse data (mainly 2D footprints, building heights and functions) seems impossible at first sight. However it succeeds by incorporating the spatial context of a building. Since the input data can be derived easily and at very low cost, this method is widely applicable. Nevertheless, as with all supervised learning algorithms, obtaining labelled training data is very time-consuming. Herewith, we provide a method which uses outlier detection and clustering methods to support users in efficiently and rapidly obtaining adequate training data.

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“…In the descriptors (input) space, one can use as AD a threshold for the distance between a test object and specially selected (as in the case of the k-center method [27] ) or the closest training set objects, as it is realized in different version of kNN approach. [6,28,29] One can also define a kernel-representing (feature) space, onto which the input space is implicitly mapped. [11] In this case, the boundary between high and low density regions is associated with the support vectors.…”

Section: One-class Classification and Probability Density Estimationmentioning

confidence: 99%

The One‐Class Classification Approach to Data Description and to Models Applicability Domain

Baskin

Kireeva

Varnek

2010

Molecular Informatics

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In this paper, we associate an applicability domain (AD) of QSAR/QSPR models with the area in the input (descriptor) space in which the density of training data points exceeds a certain threshold. It could be proved that the predictive performance of the models (built on the training set) is larger for the test compounds inside the high density area, than for those outside this area. Instead of searching a decision surface separating high and low density areas in the input space, the one-class classification 1-SVM approach looks for a hyperplane in the associated feature space. Unlike other reported in the literature AD definitions, this approach: (i) is purely "data-based", i.e. it assigns the same AD to all models built on the same training set, (ii) provides results that depend only on the initial descriptors pool generated for the training set, (iii) can be used for the huge number of descriptors, as well as in the framework of structured kernel-based approaches, e.g., chemical graph kernels. The developed approach has been applied to improve the performance of QSPR models for stability constants of the complexes of organic ligands with alkaline-earth metals in water.

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Lof

Cited by 2,842 publications

References 8 publications

Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

Automatic classification of building types in 3D city models

The One‐Class Classification Approach to Data Description and to Models Applicability Domain

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