https://nova.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 A modularity-based measure for cluster selection from clustering hierarchies https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:42794 Wed 07 Sep 2022 14:45:36 AEST ]]> Similarity-Based Unsupervised Evaluation Of Outlier Detection https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:52935 Tue 16 Jan 2024 15:57:50 AEDT ]]> Density-based clustering https://nova.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 ]]> A unified view of density-based methods for semi-supervised clustering and classification https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:42134 Thu 18 Aug 2022 15:59:20 AEST ]]> Efficient computation and visualization of multiple density-based clustering hierarchies https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:40332 Thu 14 Jul 2022 11:09:11 AEST ]]> On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study https://nova.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 ]]> CORE-SG: Efficient Computation of Multiple MSTs for Density-Based Methods https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:51926 Fri 22 Sep 2023 11:01:04 AEST ]]> Model-based clustering with HDBSCAN* https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39318 Fri 03 Jun 2022 15:28:24 AEST ]]>