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

Zhu, Ye, Ting, KM, Carman, MJ and Angelova Turkedjieva, Maia 2021, CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities, Pattern Recognition, pp. 1-41, doi: 10.1016/j.patcog.2021.107977.

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Title CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities
Author(s) Zhu, YeORCID iD for Zhu, Ye orcid.org/0000-0003-4776-4932
Ting, KM
Carman, MJ
Angelova Turkedjieva, MaiaORCID iD for Angelova Turkedjieva, Maia orcid.org/0000-0002-0931-0916
Journal name Pattern Recognition
Article ID 107977
Start page 1
End page 41
Total pages 41
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2021-04-08
ISSN 0031-3203
Keyword(s) Density-ratio
Density-based clustering
kNN anomaly detection
inhomogeneous cluster densities
Scaling
Shift
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
NORMALIZATION
Summary The problem of inhomogeneous cluster densities has been a long-standing issue for distance-based and density-based algorithms in clustering and anomaly detection. These algorithms implicitly assume that all clusters have approximately the same density. As a result, they often exhibit a bias towards dense clusters in the presence of sparse clusters. Many remedies have been suggested; yet, we show that they are partial solutions which do not address the issue satisfactorily. To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density—homogenising cluster density while preserving the cluster structure of the dataset. We show that this can be achieved by using a new multi-dimensional Cumulative Distribution Function in a transform-and-shift method. The method can be applied to every dataset, before the dataset is used in many existing algorithms to match their implicit assumption without algorithmic modification. We show that the proposed method performs better than existing remedies.
Notes In Press
Language eng
DOI 10.1016/j.patcog.2021.107977
Indigenous content off
Field of Research 0801 Artificial Intelligence and Image Processing
0806 Information Systems
0906 Electrical and Electronic Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30155369

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