Kernel-based clustering via Isolation Distributional Kernel

We propose the first clustering algorithm that employs an adaptive distributional kernel without any optimization, while achieving a similar optimization objective function.

Hierarchical clustering that takes advantage of both density-peak and density-connectivity

It formally defines a new kind of clusters and proposes a new method called DC-HDP to overcome these weaknesses to identify clusters with arbitrary shapes and varied densities. .

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

Replacing Gaussian kernel with Isolation kernel in t-SNE significantly improves the quality of the final visualisation output.

CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities

CDF-TS is a preprocessing method to transform data and equalises the density of all clusters.

Adversarial decision strategies in multiple network phased oscillators: the Blue-Green-Red Kuramoto-Sakaguchi model

We model a three-networked system of frustrated phased oscillators, with each population labelled Blue, Green and Red.

Anomaly detection of aircraft lead-acid battery

The experimental results show that the latest isolation‐based anomaly detectors, iForest and iNNE, have outstanding performance on this task and have promising applicability as efficient methods for guaranteeing the lead‐acid battery quality and reliability in civil aviation aircraft.

A technical survey on statistical modelling and design methods for crowdsourcing quality control

Our survey provides technical details on how different frameworks systematically unify crowdsourcing aspects to determine the response quality.

Cloud-assisted privacy-conscious large-scale Markowitz portfolio

The theory of Markowitz portfolio has had enormous value and extensive applications in finance since it came into being. A Markowitz model (MM) is taken into consideration for outsourcing to a public cloud in a privacy-conscious way.

Lowest probabilitymass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms

We propose to use mass-based dissimilarity, which employs estimates of the probability mass to measure dissimilarity, to replace the distance metric.

Grouping Points by Shared Subspaces for Effective Subspace Clustering

We propose a new subspace clustering framework named CSSub (Clustering by Shared Subspaces).