University of Hertfordshire

By the same authors

Instance Weighted Clustering: Local Outlier Factor and K-Means

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publicationProceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference
Subtitle of host publicationProceedings of the EANN 2020
EditorsLazaros Iliadis, Plamen Parvanov Angelov, Chrisina Jayne, Elias Pimenidis
PublisherSpringer
Pages435-446
Number of pages12
ISBN (Electronic)9783030487911
ISBN (Print)9783030487904
DOIs
Publication statusPublished - 28 May 2020

Publication series

NameProceedings of the International Neural Networks Society
PublisherSpringer
ISSN (Print)2661-8141
ISSN (Electronic)2661-815X

Abstract

Clustering is an established unsupervised learning method. Substantial research has been carried out in the area of feature weighting, as well instance selection for clustering. Some work has paid attention to instance weighted clustering algorithms using various instance weighting metrics based on distance information, geometric information and entropy information. However, little research has made use of instance density information to weight instances. In this paper we use density to define instance weights. We propose two novel instance weighted clustering algorithms based on Local Outlier Factor and compare them against plain k-means and traditional instance selection.

Notes

© 2020 Springer-Verlag. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-48791-1_34.

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