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

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. .

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

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

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

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.

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

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.

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

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