Consensus function based on cluster-wise two level clustering

Mohammad Reza Mahmoudi, Hamidreza Akbarzadeh, Hamid Parvin*, Samad Nejatian, Vahideh Rezaie, Hamid Alinejad-Rokny

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The ensemble clustering tries to aggregate a number of basic clusterings with the aim of producing a more consistent, robust and well-performing consensus clustering result. The current paper wants to introduce an ensemble clustering method. The proposed method, called consensus function based on two level clustering (CFTLC), introduces a new consensus clustering where it makes a cluster clustering task through applying an average hierarchical clustering on a cluster-cluster similarity matrix obtained by an innovative similarity metric. By applying the average hierarchical clustering algorithm, a set of meta clusters has been attained. Considering each meta cluster as a consensus cluster in the consensus clustering output, it then assigns each data point to a meta cluster through defining an object-cluster similarity. Before doing anything, CFTLC converts the primary partitions into a binary cluster representation where the primary ensemble has been broken into a number of basic binary clusters (BC). CFTLC first combines the basic BCs with the maximum cluster-cluster similarity. This step is iterated as long as a predefined number of meta clusters are ready. At the subsequent step, it assigns each data point to exactly one meta cluster. The proposed method has been experimentally compared with the state of the art clustering algorithms in terms of accuracy and robustness.

Original languageEnglish
Pages (from-to)639-665
Number of pages27
JournalArtificial Intelligence Review
Volume54
Issue number1
DOIs
Publication statusPublished - Jan 2021

Keywords

  • Consensus clustering
  • K-means
  • Similarity criterion
  • Machine learning
  • Data mining
  • COMBINING MULTIPLE CLUSTERINGS
  • COMBINATION SCHEME
  • ENSEMBLE FRAMEWORK
  • SELECTION
  • EXTRACTION
  • WISDOM
  • MATRIX

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