Hierarchical tree snipping: clustering guided by prior knowledge | Zendy

Dikla Dotan-Cohen | Zendy; Avraham A. Melkman | Zendy; Simon Kasif | Zendy

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Hierarchical tree snipping: clustering guided by prior knowledge

Author(s) -

Dikla Dotan-Cohen,

Avraham A. Melkman,

Simon Kasif

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm526

Subject(s) - cluster analysis , hierarchical clustering , computer science , tree (set theory) , java , data mining , similarity (geometry) , single linkage clustering , pattern recognition (psychology) , correlation clustering , artificial intelligence , cure data clustering algorithm , mathematics , combinatorics , image (mathematics) , programming language

Hierarchical clustering is widely used to cluster genes into groups based on their expression similarity. This method first constructs a tree. Next this tree is partitioned into subtrees by cutting all edges at some level, thereby inducing a clustering. Unfortunately, the resulting clusters often do not exhibit significant functional coherence.

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