Frequent substructure-based approaches for classifying chemical compounds

By Mukund Deshpande, Michihiro Kuramochi, George Karypis. 2003

307 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

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Citations in the corpus

Text Categorization as a Graph Classification Problem (2015, methodology): “The ﬁrst three rows correspond to the baselines: unsupervised n-gram feature extraction and then supervised learning using kNN, NB (Multinomial but Bernoulli yields similar results) and linear SVM.”
Frequent sub-structure-based approaches for classifying chemical compounds (2005, background): “An extended version of this paper is available at [6].”
Graph Invariant Kernels (2015, background): “, 2004], neighborhoods [Costa and De Grave, 2010], or arbitrary frequent patterns [Deshpande et al., 2005].”
Correlated itemset mining in ROC space: a constraint programming approach (2009, unknown): “The popularity of correlated patterns is in part due to their use for classification, where they have been used both as classification rules [30, 5, 9, 27, 13] and as features for building classifiers [6, 11].”
Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009, methodology): “We compare CORK to three state-of-the-art filter methods, namely Pearson’s Correlation Coefficient, the Sequential Cover method [8] and to the Delta Method which is closely related to MoSS [3].”
Text Classification using Graph Mining-based Feature Extraction (2010, background): “It has been established that structural information plays an important role in classification accuracy [14].”
Finding Frequent Patterns in a Large Sparse Graph* (2004, background): “…al., 2004), VLSI reverse engineering (Yoshida and Motoda, 1995), link analysis (Jensen and Goldberg, 1998; Kleinberg et al., 1999; Kleinberg, 1999; Palmer et al., 2002), and chemical compound classification (Dehaspe et al., 1998; Kramer et al., 2001; Gonzalez et al., 2001; Deshpande et al., 2003).”
Sentiment Classification using Automatically Extracted Subgraph Features (2010, methodology): “With several implementations available (3), gSpan has been commonly used for mining frequent subgraph patterns (Kudo et al., 2004; Deshpande et al., 2005).”
Mining significant graph patterns by leap search (2008, methodology): “LEAP provides a scalable approach to find discriminative subgraph patterns and one can take them as features to classify, in a way faster than other subgraph-based classification approach [15, 7, 23].”
Graph kernels based on tree patterns for molecules (2006, unknown): “Classiﬁcation of graphs is often associated with the problem of graph mining, which consists in detecting interesting patterns occurring in the graphs, and using them as features to build predictive models (King et al., 1996; Inokuchi et al., 2003; Helma et al., 2004; Deshpande et al., 2005).”