Characterization and detection of noise in clustering

Research output: Contribution to journalArticlepeer-review

554 Scopus citations

Abstract

A concept of 'Noise Cluster' is introduced such that noisy data points may be assigned to the noise class. The approach is developed for objective functional type (K-means or fuzzy K-means) algorithms, and its ability to detect 'good' clusters amongst noisy data is demonstrated. The approach presented is applicable to a variety of fuzzy clustering algorithms as well as regression analysis.

Original languageEnglish (US)
Pages (from-to)657-664
Number of pages8
JournalPattern Recognition Letters
Volume12
Issue number11
DOIs
StatePublished - Nov 1991

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Clustering
  • K-means algorithms
  • classification amongst noisy data
  • fuzzy K-means algorithms
  • noise cluster

Fingerprint

Dive into the research topics of 'Characterization and detection of noise in clustering'. Together they form a unique fingerprint.

Cite this