no code implementations • 21 Jan 2024 • Sunil Aryal, Jonathan R. Wells, Arbind Agrahari Baniya, KC Santosh
In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters.
1 code implementation • 14 Feb 2020 • Kai Ming Ting, Jonathan R. Wells, Ye Zhu
This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects.
no code implementations • 2 Jul 2019 • Kai Ming Ting, Jonathan R. Wells, Takashi Washio
A current key approach focuses on ways to produce an approximate finite-dimensional feature map, assuming that the kernel used has a feature map with intractable dimensionality---an assumption traditionally held in kernel-based methods.
no code implementations • 3 Jul 2017 • Jonathan R. Wells, Kai Ming Ting
We show that a recent outlying aspects miner can run orders of magnitude faster by simply replacing its density estimator with the proposed density estimator, enabling it to deal with large datasets with thousands of dimensions that would otherwise be impossible.