58 citations · 78 across the 7 of their papers we have counts for
8 papers
CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data
Luke W. Yerbury, Ricardo J. G. B. Campello, G. C. Livingston +2
With grid operators confronting rising uncertainty from renewable integration and a broader push toward electrification, Demand-Side Management (DSM) -- particularly Demand Respons…
Comparing Clustering Approaches for Smart Meter Time Series: Investigating the Influence of Dataset Properties on Performance
Luke W. Yerbury, Ricardo J. G. B. Campello, G. C. Livingston +2
The widespread adoption of smart meters for monitoring energy consumption has generated vast quantities of high-resolution time series data which remains underutilised. While clust…
Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)
Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello +2
Outlier detection algorithms typically assign an outlier score to each observation in a dataset, indicating the degree to which an observation is an outlier. However, these scores…
On the Use of Relative Validity Indices for Comparing Clustering Approaches
Luke W. Yerbury, Ricardo J. G. B. Campello, G. C. Livingston +2
Relative Validity Indices (RVIs) such as the Silhouette Width Criterion and Davies Bouldin indices are the most widely used tools for evaluating and optimising clustering outcomes.…
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Hanxun Huang, Ricardo J. G. B. Campello, Sarah Monazam Erfani +3
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality…
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
Alastair Anderberg, James Bailey, Ricardo J. G. B. Campello +4
We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset. Using the theory of Local Intrin…
The Area Under the ROC Curve as a Measure of Clustering Quality
Pablo Andretta Jaskowiak, Ivan Gesteira Costa, Ricardo José Gabrielli Barreto Campello
The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compell…
Efficient Computation of Multiple Density-Based Clustering Hierarchies
Antonio Cavalcante Araujo Neto, Joerg Sander, Ricardo J. G. B. Campello +1
HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter mpts. While the performa…