Efficient mining for structurally diverse subgraph patterns in large molecular databases

Machine Learning

Volumn 83, Issue 2, 2011, Pages 193-218

  Maunz, Andreas ^a   Helma, Christoph ^b   Kramer, Stefan ^c  

^a UNIVERSITY OF FREIBURG   (Germany)

^b In silico Toxicology   (Switzerland)

^c Institut für Informatik   (Germany)

Author keywords

Backbone; Correlated graph mining; Dynamic upper bound pruning; Structural diversity

Indexed keywords

BACKBONE; CLASSIFICATION ACCURACY; CLOSED FRAGMENTS; CLOSED PATTERN; CO-OCCURRENCE; CORRELATED GRAPH MINING; CROSS VALIDATION; DATA SETS; DESCRIPTORS; FEATURE SETS; FREQUENCY PARAMETERS; GRAPH MINING; LARGE PARTS; MINING ALGORITHMS; MOLECULAR DATABASE; RUNNING TIME; SEARCH SPACES; STATISTICAL CONSTRAINTS; STRUCTURAL DIVERSITY; STRUCTURAL SIMILARITY; STRUCTURE ACTIVITY RELATIONSHIPS; SUBGRAPHS; TRAINING SETS; TREE MINING; UPPER BOUND;

BINARY TREES; ENTROPY; VISUALIZATION;

PLANT EXTRACTS;

EID: 79958784191     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-010-5187-6     Document Type: Conference Paper

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