https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39098 Wed 31 Aug 2022 10:30:16 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:52935 Tue 16 Jan 2024 15:57:50 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:39118 density-based clustering, a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects. Density-based clusters are separated from each other by contiguous regions of low density of objects. Data objects located in low-density regions are typically considered noise or outliers. In this review article we discuss the statistical notion of density-based clusters, classic algorithms for deriving a flat partitioning of density-based clusters, methods for hierarchical density-based clustering, and methods for semi-supervised clustering. We conclude with some open challenges related to density-based clustering. This article is categorized under: Technologies > Data Preprocessing Ensemble Methods > Structure Discovery Algorithmic Development > Hierarchies and Trees.]]> Tue 10 May 2022 15:15:33 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42134 Thu 18 Aug 2022 15:59:20 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:40204 k nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Based on the overall performance of the outlier detection methods, we provide a characterization of the datasets themselves, and discuss their suitability as outlier detection benchmark sets. We also examine the most commonly-used measures for comparing the performance of different methods, and suggest adaptations that are more suitable for the evaluation of outlier detection results.]]> Mon 11 Jul 2022 11:13:16 AEST ]]>