Self consistency grouping

a stringent clustering method.

Bong Hyun Kim, Bhadrachalam Chitturi, Nick V. Grishin

Research output: Contribution to journalArticle

Abstract

Numerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency. Our method, self consistency grouping, i.e. SCG, yields clusters whose members are closer in rank to each other than to any member outside the cluster. We do not define a distance metric; we use the best known distance metric and presume that it measures the correct distance. SCG does not impose any restriction on the size or the number of the clusters that it finds. The boundaries of clusters are determined by the inconsistencies in the ranks. In addition to the direct implementation that finds the complete structure of the (sub)clusters we implemented two faster versions. The fastest version is guaranteed to find only the clusters that are not subclusters of any other clusters and the other version yields the same output as the direct implementation but does so more efficiently. Our tests have demonstrated that SCG yields very few false positives. This was accomplished by introducing errors in the distance measurement. Clustering of protein domain representatives by structural similarity showed that SCG could recover homologous groups with high precision. SCG has potential for finding biological relationships under stringent conditions.

Original languageEnglish (US)
JournalBMC Bioinformatics
Volume13 Suppl 13
StatePublished - 2012

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Self-consistency
Distance measurement
Clustering Methods
Grouping
Cluster Analysis
Proteins
Distance Metric
Clustering
Distance Measurement
Structural Similarity
K-means
False Positive
Inconsistency
Linkage
Restriction
Protein
Output
Protein Domains

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics
  • Structural Biology

Cite this

Self consistency grouping : a stringent clustering method. / Kim, Bong Hyun; Chitturi, Bhadrachalam; Grishin, Nick V.

In: BMC Bioinformatics, Vol. 13 Suppl 13, 2012.

Research output: Contribution to journalArticle

Kim, Bong Hyun ; Chitturi, Bhadrachalam ; Grishin, Nick V. / Self consistency grouping : a stringent clustering method. In: BMC Bioinformatics. 2012 ; Vol. 13 Suppl 13.
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