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Found 4 papers, 1 papers with code
Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing
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.
Point-Set Kernel Clustering
This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects.
Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning
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.
A simple efficient density estimator that enables fast systematic search
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.
