Clustering High-Dimensional Data

Michael E. Houle, Marie Kiermeier, Arthur Zimek

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

Clustering algorithms have been adapted or specifically designed for high-dimensional data where many attributes might be just noise such that patterns can be identified only in appropriate combinations of attributes and would be obfuscated by noise otherwise. In this chapter, we give an overview of the basic strategies and techniques used for these specialized algorithms along with pointers to example methods.
Original languageEnglish
Title of host publicationMachine Learning for Data Science Handbook : Data Mining and Knowledge Discovery Handbook
EditorsLior Rokach, Oded Maimon, Erez Shmueli
PublisherSpringer
Publication date2023
Edition3.
Pages219-237
ISBN (Print)978-3-031-24627-2, 978-3-031-24630-2
ISBN (Electronic)978-3-031-24628-9
DOIs
Publication statusPublished - 2023

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