An adaptive selection strategy and separation measure for improving the wu-huberman clustering

Yan Sun, Yiyuan Tang, Lizhu Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Clustering is an old research topic in data mining and machine learning. There are so many clustering methods, and among them Wu-Huberman is few linear algorithm. In this paper, we propose the CBWH algorithm that does not only preserve the merit of Wu-Huberman, but also increase the robustness and extend the application of the Wu-Huberman. At first, we extend the Wu-Huberman to the general clustering problem by constructing graph. Then, we present a new idea to determine the threshold of the clustering. Additionally, we provide experiments for analyzing the effectiveness of the algorithm, comparing with other related algorithms and discussing the sensitivity, from which we find that CBWH is more robust.

Original languageEnglish
Pages (from-to)1531-1536
Number of pages6
JournalICIC Express Letters, Part B: Applications
Volume3
Issue number6
StatePublished - 2012

Keywords

  • Clustering
  • Data mining
  • KNN (k nearest neighborhood)
  • Wu-Huberman

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